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Brain frequency based handicap wheelchair
- 1. Brain Frequency Based Handicap Wheelchair
- 2. Team Details Mentor: K DHANUARAVINTH – 18bee080@mcet.in - +91 6379156972 R VISHNU RAJ – 18bee087@mcet.in - +91 8220396913 DR K UMAMAHESWARI – umamaheswari@drmcet.ac.in - +91 8825843655
- 3. Sustainable Development Goal Good Health & Well being
- 4. Problem Statement ❑ People who are using wheelchair require secondary assistance to carry out their basic and daily activities, this greatly reduces their esteem and will power. ❑ The main problem of the wheelchair is that cannot be used by disabled person, so the type of artificial aid needed by a disabled person in order to move about depends, to a large extent, on the level of his incapacity. According to our output algorithm the control of our wheelchair is achieved According to our output algorithm the control of our wheelchair is achieved
- 5. Statistics of Disabled People by MOSPI sector graph 1.98 1.92 2.2 2.01 2.37 2.18 2.68 2.41 2.18 2.05 2.45 2.21 0 0.5 1 1.5 2 2.5 3 Other than SC/ ST ST SC Total Persons Males Females 5 Brain Frequency Based Handicap Wheelchair Need for System:
- 6. Distribution of disabled persons (%) in India – Census 2011 6 Brain Frequency Based Handicap Wheelchair Among the disabled persons, most of them are in the category of Movement disability
- 7. Disabled population in various age groups Brain Frequency Based Handicap Wheelchair 7 Among the disabled persons, most of them areYoungsters (Active persons)
- 8. Statistics in India ❑ According to the statistics of the Ministry of Statistics and Programme Implementation in 2016, In India out of the 121 Crore population, 2.68 Crore persons are 'disabled' which is 2.21% of the total population which is more than the entire population of Australia.
- 9. Statistics in World ❑ According to the statistics of the World Health Organization in 2011, About 15% of the world's population lives with some form of disability, of whom 2-4% experience significant difficulties in functioning.
- 10. Solution of the Problem ❑ EEG Based Wheelchair Recording the Brain wave by scalp electrodes and classify the signals, at last fed into the electric motor for imagined movements.
- 11. Brain Computer Interface Direct communication pathway between an enhanced or wired brain and an external device
- 13. Introduction Brain Computer Interface is a fastest growing emerging technology, in which researchers aim to build a direct channel between the human brain and the computer
- 14. Principle Behind BCI This technology is based on to sense, transmit, analyze, and apply the language of neuron
- 15. BCI TYPES
- 16. Non Invasive The sensors are placed on the scalp to measure the electrical potentials produced by the brain (EEG) or the magnetic field (MEG)
- 17. Used Method – Non Invasive EEG Electroencephalography
- 18. EEG Electroencephalography Definition EEG provides the recording of electrical activity of the brain from the surface of the scalp
- 19. How does it work ? Electrodes are placed on the scalp to pickup the electrical current generated by the brain Neuron
- 20. Brain Response Motor Cortex
- 21. When groups of neurons fire together, they provide enough signal for us to measure from the scalp How does it work ?
- 23. Process of EEG Wheelchair
- 24. “ Flow Diagram • Signal Acquisition First thing we need is some raw EEG data to process. This data is usually not clean so some preprocessing steps are needed • Signal Preprocessing The application of filters are applied, such as a high-pass filter and low pass filter
- 25. Preprocessing Tool Brain Frequency Based Handicap Wheelchair 25 • EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high- density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods.
- 26. “ Flow Diagram • Feature Extraction We can apply complex automatic processing algorithms that allow us to extract ‘hidden’ information from EEG signals. • Signals Classification For instance, all brain-computer interface systems follow this common scheme, in which the classification step is performed in order to decide what the user is thinking.
- 27. “ Flow Diagram • Control Actions According to our output algorithm the control of our wheelchair is achieved • Output(Wheelchair Motor) Finally the EEG taught for directions of wheelchair is precisely controlled with sensor braking system
- 28. Software – EEGLAB Matlab
- 29. Technology Used ❑ Internet of Things
- 30. The Internet of Things refers to the billions of physical devices around the world that are now connected to the internet, all collecting and sharing data. What is the Internet of Things?
- 31. Analysis of Recorded Signals
- 36. ❑ By collaborating with medical institutes, we can provided our Brain Interface wheelchairs to person in recrupation. ❑ We can increase the accuracy and efficiency of the device by collaborating with more detailed neuroscience medical research team and their works. ❑ We can collaborate with defense and research organization, to make the wheelchairs free for injured war soldiers to enhance their quality of life. Implementation Plan
- 37. Current Scenario
- 38. Army Injuries
- 40. Accidents
- 41. What it is ?
- 42. Invasive The micro-electrodes are placed directly into the cortex, measuring the activity of a single neuron
- 43. Partial Invasive The electrodes are placed on the exposed surface of the brain(ECoG)
- 46. The letters F, T, C, P, and O stand for Frontal, Temporal, Central, Parietal and Occipital. Even numbers (2, 4, 6, 8) refer to the right hemisphere and odd numbers (1, 3, 5, 7) refer to the left hemisphere. The z refers to an electrode placed on the midline.
- 54. TRANSFER BLOCKS
- 56. Comparison with Other Methods EEG has low spatial but high temporal resolution. Currently, fMRI and MEG rely on expensive, bulky equipment; PET requires the injection of a radioactive substance into the bloodstream. Thus, methods relying on NIRS and, in particular, EEG, are most commonly used
- 61. Band Frequency (Hz) Location Normally Pathologically Delta < 4 Frontally in adults, Posteriorly in children; high - amplitude waves Adult slow- wave sleep in babies has been found during some continuous- attention tasks Subcortical lesions diffuse lesions metabolic encephalopathy hydrocephalus deep midline lesions
- 62. Band Frequency (Hz) Location Normally Pathologically Theta 4 - 7 Found in locations not related to task at hand Higher in young children drowsiness in adults and teens Idling Associated with inhibition of elicited responses (has been found to spike in situations where a person is actively trying to repress a response or action) Focal subcortical lesions metabolic encephalopathy deep midline disorders some instances of hydrocephalus
- 63. Band Frequency (Hz) Location Normally Pathologically Alpha 8 - 15 Posterior regions of head, both sides, higher in amplitude on dominant side. Central sites (c3-c4) at rest Relaxed/reflecting closing the eyes Also associated with inhibition control, seemingly with the purpose of timing inhibitory activity in different locations across the brain Coma
- 64. Band Frequency (Hz) Location Normally Pathologically Beta 16 - 31 Both sides, symmetrical distribution, most evident frontally; low- amplitude waves Range span: active calm → intense → stressed → mild obsessive active thinking, focus, high alert, anxious Benzodiazepines Dup15q syndro me
- 65. Band Frequency (Hz) Location Normally Pathologically Gamma > 32 Somatosensory cortex Displays during cross- modal sensory processing (perception that combines two different senses, such as sound and sight) Also is shown during short-term memory matching of recognized objects, sounds, or tactile sensations A decrease in gamma- band activity may be associated with cognitive decline, especially when related to the theta band; however, this has not been proven for use as a clinical diagnostic measurement
- 66. Band Frequency (Hz) Location Normally Pathologically Mu 8 - 12 Sensorimotor cortex Shows rest-state motor neurons Mu suppression could indicate that motor mirror neurons are working. Deficits in Mu suppression, and thus in mirror neurons, might play a role in autism
- 67. When and Where it is use ?
- 68. • Brain tumor • Brain damage from head injury • Brain dysfunction that can have a variety of causes (encephalopathy) • Inflammation of the brain (encephalitis) • Stroke • Head Injury • Sleep disorders and more… Medical Use
- 69. • Teleoperation of an Industrial Manipulator through a TCP/IP Channel • Special machines Controls and More… Engineering Uses
- 70. Research Area • Neuroscience • Cognitive Science • Cognitive Psychology • Neurolinguistics • Psychophysiological Research
- 73. ❑NeuroScan ❑Brain Products ❑Bio Semi ❑EGI ❑Emotiv Top 10 EEG Companies ❑ NeuroSky ❑ Advanced Brain Monitoring ❑ g tec ❑ ANT Neuro ❑ Neuroelectrics
- 74. ❑ Neurosky Brainwave – INR 15,500 Rs ❑ Niscomed Electroencephalogram (EEG) Machine – INR 98,280 Rs
- 75. Market Values
- 76. Area to be Exploited: ❑ Towards commercialization ❑ New Innovations ❑ Research The Global Electric Wheelchair Market was valued at US$ 2,911.5 Million in 2018, and is expected to witness a Compound Annual Growth Rate (CAGR) of 17.1% during the forecast period (2018 – 2026).
- 78. TARGET ❑ Low Cost ❑ High Features ❑ More Safety ❑ Available for Children and Both genders ❑ Tracking System ❑ Long Lasting Battery
- 79. Limitations
- 80. ❑ Lack of sensor modality that provides safe, accurate, and robust, access to brain Signals ❑ Very Expensive ❑ Information transformation rate is limited ❑ Difficult to adaptation and learning
- 81. Hardware Description • Neurosky Mind wave mobile Headset or OpenBCI headset electrodes • Nodemcu esp8266 Wi-Fi module • Bldc wheel motors • Hybrid Wheelchair • Ultrasonic sensors • Gsm module • Heavy duty battery’s • OpensourceBCI tool or EEGLAB embedded tool
- 83. NeuroEntertainment ❑ Neurogaming ❑ Virtual Reality ❑ Neuro Toys ❑ Art
- 84. Security ❑ Brain based authentication
- 85. Biofeedback Therapy ❑ Anxiety ❑ Sleep Improvement ❑ ADHD and PTSD
- 86. Cognitive Training ❑ Performance Optimization ❑ Brain Ageing ❑ Early Development ❑ Mindfulness ❑ Accelerated Learning ❑ Enhanced Creativity
- 87. Implementation Plan Timeline Q1. 1 Month Related Literature survey Q2. 3 Months Finalization of architecture and system design Q3. 1 Month Testing Q4. 12 Months Next to Outreach plan 87 Brain Frequency Based Handicap Wheelchair
- 88. Outreach Plan Timeline Q1. 1-2 Months Business Pattern Q2. 3-4 Months Approval, Authorization and Installation of Business Q3. 5-6 Months Advertising – Digital Marketing ,Village campaigns, primary health centers Q4. 5-6 Months Retail and wholesale – Retail using Digital platforms and Whole sales using traditional marketing 88 Brain Frequency Based Handicap Wheelchair
- 89. “ • By collaborating with medical institutes, we can provided our Brain Interface wheelchairs to person in recreation • Collaborating with Insurance companies and offering this wheelchair at the time of accidents and health risks • We can collaborate with defense and research organization, to make the wheelchairs free for injured war soldiers to enhance their quality of life Implementation and outreach plan
- 90. Business & Employee Goals Business priorities ▪ Increase customer satisfaction ▪ Maintain growth ▪ Diversify investment ▪ Initiative partnership with 3rd party organizations Employee opportunities ▪ Multi-disciplinary employment ▪ Worker can Improve their skillsets 90 Brain Frequency Based Handicap Wheelchair
- 91. Summary Our business is good High level Profit We’re getting our work done We finished the consolidation project Our customers relationship We increased customer retention 91 Brain Frequency Based Handicap Wheelchair
- 92. Projects ❑ BrainGate ❑ BCI2000 ❑ Australian Bionic Eye ❑ Honda Asimo Control ❑ Kevin Warwick- The first human Cyborg
- 94. References 1) Fast fourier analysis and EEG classification brainwave controlled wheelchair – DOI - 10.1109/CCSSE.2016.7784344 2) An Internet of Things (IoT) application to control a wheelchair through EEG signal processing – DOI - 10.1109/WEROB.2017.8383877 3) Design of a Brain Controlled Wheelchair – DOI - 10.1109/CCSSE.2018.8724794 4) Design of an EEG-Based Brain Controlled Wheelchair for Quadriplegic Patients – DOI - 10.1109/I2CT.2018.8529751 5) EEG Wheelchair for People of Determination – DOI - 10.1109/ASET48392.2020.9118340 6) Wheelchair Neuro Fuzzy Control Using Brain Computer Interface – DOI - 10.1109/DeSE.2019.00120
- 95. Thank You