1) The document presents a study that uses densely connected 3D convolutional neural networks to map ECoG brain activity recorded during speech production to speech audio.
2) ECoG signals were recorded from six participants as they read words aloud and fed into a DenseNet model to reconstruct spectrograms, which were then used by a WaveNet model to reconstruct audio waveforms.
3) The reconstructed speech audio was of high quality, achieving correlations up to 0.69 between original and reconstructed spectrograms, showing that the model learned to distinguish silence from speech and capture speech dynamics from neural signals. This is the first time high quality reconstructed speech audio has been achieved from neural recordings using deep learning.
Identify objects based on modeling the human visual system, as an effective method in intelligent identification, has attracted the attention of many researchers.Although the machines have high computational speed but are very weak as compared to humans in terms of diagnosis. Experience has shown that in many areas of image processing, algorithms that have biological backing had more simplicity and better performance. The human visual system, first select the main parts of the image which is provided by the visual featured model, then pays to object recognition which is a hierarchical operations according to this, HMAX model is also provided. HMAX object recognition model from the group of hierarchical models without feedback that its structure and parameters selected based on biological characteristics of the visual cortex. This model is a hierarchical model neural network with four layers, is composed of alternating layers that are simple and complex. Due to the high complexity of the human visual system is virtually impossible to replicate it. For each of the above, separate models have been proposed but in the human visual system, this operation is performed seamlessly, thus, by combining the principles of these models is expected to be closer to the human visual system and obtain a higher recognition rate. In this paper, we introduce an architecture to classify images based on a combination of previous work is based on the basic operation of the visual cortex. According to the results presented, the proposed model compared with the main HMAX model has a much higher recognition rate. Simulations was performed on the database of Caltech101.
INVITEDP A P E RSilicon-Integrated High-DensityElectro.docxvrickens
INVITED
P A P E R
Silicon-Integrated High-Density
Electrocortical Interfaces
This paper examines the state of the art of chronically implantable
electrocorticography (ECoG) interface systems and introduces a novel modular
ECoG system using an encapsulated neural interfacing acquisition chip (ENIAC)
that allows for improved, broad coverage in an area of high spatiotemporal
resolution.
By Sohmyung Ha, Member IEEE, Abraham Akinin, Student Member IEEE,
Jiwoong Park, Student Member IEEE, Chul Kim, Student Member IEEE,
Hui Wang, Student Member IEEE, Christoph Maier, Member IEEE,
Patrick P. Mercier, Member IEEE, and Gert Cauwenberghs, Fellow IEEE
ABSTRACT | Recent demand and initiatives in brain research
have driven significant interest toward developing chronically
implantable neural interface systems with high spatiotempo-
ral resolution and spatial coverage extending to the whole
brain. Electroencephalography-based systems are noninva-
sive and cost efficient in monitoring neural activity across the
brain, but suffer from fundamental limitations in spatiotem-
poral resolution. On the other hand, neural spike and local
field potential (LFP) monitoring with penetrating electrodes
offer higher resolution, but are highly invasive and inade-
quate for long-term use in humans due to unreliability in
long-term data recording and risk for infection and inflamma-
tion. Alternatively, electrocorticography (ECoG) promises a
minimally invasive, chronically implantable neural interface
with resolution and spatial coverage capabilities that, with
future technology scaling, may meet the needs of recently
proposed brain initiatives. In this paper, we discuss the chal-
lenges and state-of-the-art technologies that are enabling
next-generation fully implantable high-density ECoG inter-
faces, including details on electrodes, data acquisition front-
ends, stimulation drivers, and circuits and antennas for
wireless communications and power delivery. Along with
state-of-the-art implantable ECoG interface systems, we
introduce a modular ECoG system concept based on a fully
encapsulated neural interfacing acquisition chip (ENIAC).
Multiple ENIACs can be placed across the cortical surface,
enabling dense coverage over wide area with high spatio-
temporal resolution. The circuit and system level details of
ENIAC are presented, along with measurement results.
KEYWORDS | BRAIN Initiative; electrocorticography; neural
recording; neural stimulation; neural technology
I. INTRODUCTION
The Brain Research through Advancing Innovative Neuro-
technologies (BRAIN) Initiative envisions expanding our
understanding of the human brain. It targets development
and application of innovative neural technologies to ad-
vance the resolution of neural recording, and stimulation
toward dynamic mapping of the brain circuits and process-
ing [1], [2]. These advanced neurotechnologies will enable
new studies and experiments to augment our current unde ...
Identify objects based on modeling the human visual system, as an effective method in intelligent identification, has attracted the attention of many researchers.Although the machines have high computational speed but are very weak as compared to humans in terms of diagnosis. Experience has shown that in many areas of image processing, algorithms that have biological backing had more simplicity and better performance. The human visual system, first select the main parts of the image which is provided by the visual featured model, then pays to object recognition which is a hierarchical operations according to this, HMAX model is also provided. HMAX object recognition model from the group of hierarchical models without feedback that its structure and parameters selected based on biological characteristics of the visual cortex. This model is a hierarchical model neural network with four layers, is composed of alternating layers that are simple and complex. Due to the high complexity of the human visual system is virtually impossible to replicate it. For each of the above, separate models have been proposed but in the human visual system, this operation is performed seamlessly, thus, by combining the principles of these models is expected to be closer to the human visual system and obtain a higher recognition rate. In this paper, we introduce an architecture to classify images based on a combination of previous work is based on the basic operation of the visual cortex. According to the results presented, the proposed model compared with the main HMAX model has a much higher recognition rate. Simulations was performed on the database of Caltech101.
INVITEDP A P E RSilicon-Integrated High-DensityElectro.docxvrickens
INVITED
P A P E R
Silicon-Integrated High-Density
Electrocortical Interfaces
This paper examines the state of the art of chronically implantable
electrocorticography (ECoG) interface systems and introduces a novel modular
ECoG system using an encapsulated neural interfacing acquisition chip (ENIAC)
that allows for improved, broad coverage in an area of high spatiotemporal
resolution.
By Sohmyung Ha, Member IEEE, Abraham Akinin, Student Member IEEE,
Jiwoong Park, Student Member IEEE, Chul Kim, Student Member IEEE,
Hui Wang, Student Member IEEE, Christoph Maier, Member IEEE,
Patrick P. Mercier, Member IEEE, and Gert Cauwenberghs, Fellow IEEE
ABSTRACT | Recent demand and initiatives in brain research
have driven significant interest toward developing chronically
implantable neural interface systems with high spatiotempo-
ral resolution and spatial coverage extending to the whole
brain. Electroencephalography-based systems are noninva-
sive and cost efficient in monitoring neural activity across the
brain, but suffer from fundamental limitations in spatiotem-
poral resolution. On the other hand, neural spike and local
field potential (LFP) monitoring with penetrating electrodes
offer higher resolution, but are highly invasive and inade-
quate for long-term use in humans due to unreliability in
long-term data recording and risk for infection and inflamma-
tion. Alternatively, electrocorticography (ECoG) promises a
minimally invasive, chronically implantable neural interface
with resolution and spatial coverage capabilities that, with
future technology scaling, may meet the needs of recently
proposed brain initiatives. In this paper, we discuss the chal-
lenges and state-of-the-art technologies that are enabling
next-generation fully implantable high-density ECoG inter-
faces, including details on electrodes, data acquisition front-
ends, stimulation drivers, and circuits and antennas for
wireless communications and power delivery. Along with
state-of-the-art implantable ECoG interface systems, we
introduce a modular ECoG system concept based on a fully
encapsulated neural interfacing acquisition chip (ENIAC).
Multiple ENIACs can be placed across the cortical surface,
enabling dense coverage over wide area with high spatio-
temporal resolution. The circuit and system level details of
ENIAC are presented, along with measurement results.
KEYWORDS | BRAIN Initiative; electrocorticography; neural
recording; neural stimulation; neural technology
I. INTRODUCTION
The Brain Research through Advancing Innovative Neuro-
technologies (BRAIN) Initiative envisions expanding our
understanding of the human brain. It targets development
and application of innovative neural technologies to ad-
vance the resolution of neural recording, and stimulation
toward dynamic mapping of the brain circuits and process-
ing [1], [2]. These advanced neurotechnologies will enable
new studies and experiments to augment our current unde ...
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
y
information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
VOWEL PHONEME RECOGNITION BASED ON AVERAGE ENERGY INFORMATION IN THE ZEROCROS...ijistjournal
Speech signal is modelled using the average energy of the signal in the zerocrossing intervals. Variation of these energies in the zerocrossing interval of the signal is studied and the distribution of this parameter through out the signal is evaluated. It is observed that the distribution patterns are similar for repeated utterances of the same vowels and varies from vowel to vowel. Credibility of the proposed parameter is verified over five Malayalam (one of the most popular Indian language) vowels using multilayer feed forward artificial neural network based recognition system. The performance of the system using additive white Gaussian noise corrupted speech is also studied for different SNR levels. From the experimental results it is evident that the average energy information in the zerocrossing intervals and its distributions can be effectively utilised for vowel phone classification and recognition.
In recent years, unspoken words recognition has
received substantial attention from both the scientific research
communities and the society of multimedia information access
networks. Major advancements and wide range of applications
in aids for the speech handicapped, speech pathology research,
telecom privacy issues, cursor based text to speech, firefighters
wearing pressurized suits with self contained breathing
apparatus (SCBA), astronauts performing operations in
pressurized gear, as a part of communication system operating
in high background noise have propelled words recognition
technology into the spotlight. Though early words recognition
techniques used simple maximum likelihood algorithms only
but the recognition process has now graduated into a science
of mathematical representations and comparison processes.
This survey paper provides an up-to-date review of the existing
approaches and offers some insights into the study of unspoken
words recognition. A number of typical techniques and EMG
based approaches are discussed in this paper. Furthermore, a
discussion outlining the incentives for using recognition
techniques, the applications of this technology, and some of
the difficulties plaguing the current systems with regard to
this topic have also been provided.
A monkey model of auditory scene analysisPradeepD32
My work impacts half the world who develop age-related hearing loss with difficulty understanding speech in noise. To understand how the brain solves the cocktail party problem, I need to record from neurons suitable only in animals. Monkeys are best suited for this given our similar auditory brains. I use sounds without semantics and employ fMRI to show that monkeys use similar brain regions as humans to separate overlapping sounds. This study is the first to show such evidence in any animal. Now, I can record from monkey neurons and generalize the results to humans!
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
System Architecture for Brain-Computer Interface based on Machine Learning an...ShahanawajAhamad1
Brain functions are required to be read for curing
neurological illness. Brain-Computer Interface (BCI) connects
the brain to the digital world for brain signals receiving,
recording, processing, and comprehending. With a BrainComputer Interface (BCI), the information from the user’s brain
is fed into actuation devices, which then carry out the actions
programmed into them. The Internet of Things (IoT) has made it
possible to connect a wide range of everyday devices.
Asynchronous BCIs can benefit from an improved system
architecture proposed in this paper. Individuals with severe
motor impairments will particularly get benefit from this feature.
Control commands were translated using a rule-based
translation algorithm in traditional BCI systems, which relied
only on EEG recordings of brain signals. Examining BCI
technology’s various and cross-disciplinary applications, this
argument produces speculative conclusions about how BCI
instruments combined with machine learning algorithms could
affect the forthcoming procedures and practices. Compressive
sensing and neural networks are used to compress and
reconstruct ECoG data presented in this article. The neural
networks are used to combine the classifier outputs adaptively
based on the feedback. A stochastic gradient descent solver is
employed to generate a multi-layer perceptron regressor. An
example network is shown to take a 50% compression ratio and
89% reconstruction accuracy after training with real-world,
medium-sized datasets as shown in this paper
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
y
information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
VOWEL PHONEME RECOGNITION BASED ON AVERAGE ENERGY INFORMATION IN THE ZEROCROS...ijistjournal
Speech signal is modelled using the average energy of the signal in the zerocrossing intervals. Variation of these energies in the zerocrossing interval of the signal is studied and the distribution of this parameter through out the signal is evaluated. It is observed that the distribution patterns are similar for repeated utterances of the same vowels and varies from vowel to vowel. Credibility of the proposed parameter is verified over five Malayalam (one of the most popular Indian language) vowels using multilayer feed forward artificial neural network based recognition system. The performance of the system using additive white Gaussian noise corrupted speech is also studied for different SNR levels. From the experimental results it is evident that the average energy information in the zerocrossing intervals and its distributions can be effectively utilised for vowel phone classification and recognition.
In recent years, unspoken words recognition has
received substantial attention from both the scientific research
communities and the society of multimedia information access
networks. Major advancements and wide range of applications
in aids for the speech handicapped, speech pathology research,
telecom privacy issues, cursor based text to speech, firefighters
wearing pressurized suits with self contained breathing
apparatus (SCBA), astronauts performing operations in
pressurized gear, as a part of communication system operating
in high background noise have propelled words recognition
technology into the spotlight. Though early words recognition
techniques used simple maximum likelihood algorithms only
but the recognition process has now graduated into a science
of mathematical representations and comparison processes.
This survey paper provides an up-to-date review of the existing
approaches and offers some insights into the study of unspoken
words recognition. A number of typical techniques and EMG
based approaches are discussed in this paper. Furthermore, a
discussion outlining the incentives for using recognition
techniques, the applications of this technology, and some of
the difficulties plaguing the current systems with regard to
this topic have also been provided.
A monkey model of auditory scene analysisPradeepD32
My work impacts half the world who develop age-related hearing loss with difficulty understanding speech in noise. To understand how the brain solves the cocktail party problem, I need to record from neurons suitable only in animals. Monkeys are best suited for this given our similar auditory brains. I use sounds without semantics and employ fMRI to show that monkeys use similar brain regions as humans to separate overlapping sounds. This study is the first to show such evidence in any animal. Now, I can record from monkey neurons and generalize the results to humans!
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
System Architecture for Brain-Computer Interface based on Machine Learning an...ShahanawajAhamad1
Brain functions are required to be read for curing
neurological illness. Brain-Computer Interface (BCI) connects
the brain to the digital world for brain signals receiving,
recording, processing, and comprehending. With a BrainComputer Interface (BCI), the information from the user’s brain
is fed into actuation devices, which then carry out the actions
programmed into them. The Internet of Things (IoT) has made it
possible to connect a wide range of everyday devices.
Asynchronous BCIs can benefit from an improved system
architecture proposed in this paper. Individuals with severe
motor impairments will particularly get benefit from this feature.
Control commands were translated using a rule-based
translation algorithm in traditional BCI systems, which relied
only on EEG recordings of brain signals. Examining BCI
technology’s various and cross-disciplinary applications, this
argument produces speculative conclusions about how BCI
instruments combined with machine learning algorithms could
affect the forthcoming procedures and practices. Compressive
sensing and neural networks are used to compress and
reconstruct ECoG data presented in this article. The neural
networks are used to combine the classifier outputs adaptively
based on the feedback. A stochastic gradient descent solver is
employed to generate a multi-layer perceptron regressor. An
example network is shown to take a 50% compression ratio and
89% reconstruction accuracy after training with real-world,
medium-sized datasets as shown in this paper
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
Push towards digital Library .Data collection through survey. Small scale experiment .Proceeded on library area .Building a world wide digital library.
Global routing topology.
loop-free paths . Dynamic distributed algorithm was rooted on this algorithm . Take Less Recovery Time(h) than previous algorithm (h^2) when Network Links fails .
Problem analysis of MapReduce .
Mapreduce performs poorly in iterative why ?
Hadoop does not function well for random access to its datasets . But YARN promise to support that .
Why Hadoop do not support broadcasting ?
JAVA do not support sharing references during mapping task .
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Chapter 3 - Islamic Banking Products and Services.pptx
Brain signal seminar
1. Scientific Reports - Nature
Speech synthesis from ECoG using
densely connected 3D convolutional neural
networks
-
Miguel Angrick, Christian Herff, Emily Mugler , Matthew C Tate ,
Marc W Slutzky , Dean J Krusienski and Tanja Schultz
16 April 2019
NAFIZ ISHTIAQUE AHMED
LITERATURE REVIEW
UOU – UNIVERSITY OF ULSAN
2. 소개
간단한 선형 모델은 신경 활동과 지속적인 구어 연설 사이의
관계를 포착 할 수 없습니다.
기사에서는 심 신경 네트워크를 사용하여 ECoG를 매핑하여
음성을 생성 할 수 있음을 보여줍니다.
3. Sogae
gandanhan seonhyeong model-eun singyeong hwaldong-gwa
jisogjeog-in gueo yeonseol saiui gwangyeleul pochag hal su eobs-
seubnida.
gisa-eseoneun sim singyeong neteuwokeuleul sayonghayeo
ECoGleul maepinghayeo eumseong-eul saengseong hal su
iss-eum-eul boyeojubnida.
4. Introduction
ECoG signals which supplies the necessary
temporal and spatial resolution could provide a
fast and natural way of communication to
people with neurological diseases.
However; simple linear models are not good
enough to make the relation between neural
activity with continuous spoken speech.
Thus; deep neural networks can be used to
map ECoG from speech production areas onto
an intermediate representation of speech.
5. Introduction
Brainstem stroke can result in a loss of this ability to speak.
Where; BCI with ECoG is particularly is well-suited for the
decoding of speech processes from Invasively-measured
brain activity.
Densely-connected convolutional neural networks is applied
on ECoG data that results reconstructing high-quality audio
from neural signals during speech production.
6. Experiment
ECoG from six native English speaking participants. All
subjects had normal speech and language function and
normal hearing.
ECoG was recorded with a medium-density, 64-channel, 8
× 8 electrode grid.
Participants read between 244 and 372 single words shown
to them on a screen.
7. Architecture of the Decoding approach
ECoG features for each time window are fed into DenseNet regression
model to reconstruct the logarithmic mel-scaled spectrogram. Wavenet
is then used to reconstruct an audio waveform from the spectrogram.
8. Reconstruction performance
(a) Pearson correlation coefficients between original and reconstructed
spectrograms for each participant. Bars indicate the mean over all
logarithmic mel-scaled coefficients
(b) Detailed performance across all spectral bins for participant 5.
9. Reconstruction example for visual inspection
(a) compares a time-aligned excerpt of participant 5
(b) generated waveform representation of the same excerpt as in the
spectrogram comparison.
10. Discussion
It is evident that the model has learned a distinguishable
representation between silence and acoustic speech and
captures many of the intricate dynamics of human speech.
This network transforms the measured brain activity to
spectral features of speech. Correlations up to r = 0.69
across all frequency bands were achieved by this network
This is the first time that high quality audio of speech has
been reconstructed from neural recordings of speech
production using deep neural networks.