Brain computer interface using machine learning

National Institute of Technology, Patna , 22 March 2021
Contents
 Introduction to BCI
 Types of BCI
 Problem statement
 Literature survey
 Working of BCI
 Proposed method
 Applications
 Conclusion
National Institute of Technology Goa, January 2024

Introduction
Figure 1: Brain Computer Interface
What is a Brain-Computer Interface?
• Brain-Computer Interface (BCI) is a
control mechanism that evaluates
human brain activity pattern to
promote communication between
the brain and computers
• It does not depend on input from
peripheral nerves or muscles.

Basic components of BCI
Figure 2: Components of Brain Computer Interfaces

Types of BCI
1. Invasive
• Multielectrode array of tens to hundreds of electrodes implanted into brain
cortical tissue from which “movement intent” is decoded.
• They allow recording of action potentials (the acknowledged output signals
of neurons) at the millisecond timescale.
• Greater spatial resolution.

Invasive
• Electrocorticography (ECoG) is a technique for recording brain signals that
involves placing electrodes on the surface of the brain by surgical incision.
Figure 3. (A) Electrode array is placed under the dura onto the brain surface (B) X-ray image of the skull showing the
location of the electrode array.
A B

Types of BCI
2. Non-invasive
• Records signals from the brain using electrodes placed on the scalp without
harming the brain tissues.
 Magnetoencephalography (MEG)
• It records magnetic fields produced as a result of
neural activity generated in response to a stimulus.
• High temporal resolution
• No distortions
• Expensive, bulky and not portable, require
magnetically shielded room Figure 4. Example MEG system

Non-invasive
 Electroencephalography (EEG)
• The signals are recorded by placing
metal electrodes on the scalp.
• EEG signals reflect the summation of
postsynaptic potentials from many
thousands of neurons
• Captures electrical activity in the
cerebral cortex.
• Poor spatial resolution
Figure 5. Subject wearing EEG cap

Non-invasive
 Functional Magnetic Resonance Imaging (fMRI)
• Detects changes in blood flow due to increased
activation of neurons in particular brain areas
during specific tasks
• The signal recorded by fMRI is called the blood
oxygenation level dependent (BOLD) response.
• High spatial resolution.
Figure 6. fMRI machine with a subject whose brain is being scanned while performing an experiment.
The subject is holding a button-press device for indicating choices or outputs

Non-invasive
 Functional Near Infrared (fNIR) Imaging
• Technique for measuring changes in blood oxygenation level
• Based on detecting near-infrared light absorbance of hemoglobin in
the blood with and without oxygen
• More prone to noise, less spatial resolution, less expensive than fMRI,
portable.
 Positron Emission Tomography (PET)
• It is an older technique for measuring brain activity indirectly by
detecting metabolic activity.
• It measures the neural activity by injecting a nuclear substance-
emitting positron into the bloodstream.

Applications of BCI
Replace or restore CNS functioning
lost with sickness or by accident
Replace CNS functioning lost due to
diseases such as paralysis and spinal
cord injury due to stroke or trauma
Diagnose schizophrenia, brain
tumours, parkinson’s disease ETC.
Stroke rehabilitation
Transportation monitoring
Industrial robotics, increasing worker
safety by keeping people away from
potentially demanding jobs
Make games more user-
friendly

Motor Imagery
• It is a cognitive process of mentally simulating movements
without physically performing them
•This mental simulation involves the activation of the same
neural networks that are involved in actual movement
execution, including the primary motor cortex and the
supplementary motor area.
•The goal of the motor imagery classification task is to
accurately predict whether a person is imagining a specific
movement or not based on the EEG signals
Figure 7: The Neurofunctional Architecture of Motor Imagery

Literature Survey
 Subject-Independent Brain–Computer
Interfaces Based on Deep Convolutional Neural
Networks
 A Deep Transfer Convolutional Neural
Network Framework for EEG Signal
Classification
 Attention-Inception and Long- Short-Term
Memory-Based Electroencephalography
Classification for Motor Imagery Tasks in
Rehabilitation
1
Xu et al.” A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification, IEEE Access, 2019.
Kwon et al. “Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks” IEEE transactions on neural netwo
and learning systems, 2019.
Amin et al. “Attention-Inception and Long- Short-Term Memory-Based Electroencephalography Classification for Motor Imagery Tasks in
Rehabilitation” IEEE Transactions on Industrial Informatics, 2021.
2
3
1
2
3
Two-class MI tasks (movement imagination of the left
or right hand), STFT, CNN, Accuracy-74.2
CNN, Accuracy for subject specific- 71.3
Accuracy for subject independent- 74.15
CNN, LSTM, Attention
Accuracy for subject specific- 82.8

Literature Survey
 Motor Imagery Classification for Brain
Computer Interface Using Deep Convolutional
Neural Networks and Mixup Augmentation
 A Deep Learning Framework for Decoding
Motor Imagery Tasks of the Same Hand
Using EEG Signals5
Alwasiti et al.” Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation”,
open Journal of Engineering in Medicine and Biology, 2022.
Alazrai et al. “A deep learning framework for decoding motor imagery tasks of the same hand using EEG signals” IEEE Access, 2019.
Tabar et al. “A novel deep learning approach for classification of EEG motor imagery signals”, Journal of neural engineering, 2016.
6
5
6
Mixup Augmentation, Stockwell Transform for pre-
processing, CNN, Accuracy- 93
A novel three-stage framework for decoding
MI tasks of the same hand, CWD, sliding window, CNN
Accuracy-73.2
 A novel deep learning approach for
classification of EEG motor imagery signals
STFT, deep learning, BCI Competition IV
dataset, Accuracy- 74.8
4
4

Signal acquisition
1. Collect raw EEG data
Figure 8: Raw EEG data

Channel
2. Channel selection
Figure 9. International 10–20 system for standardized EEG electrode locations on the head. C = central, P = parietal, T = temporal, F
= frontal, Fp = frontal polar, O = occipital, A = mastoids

3. Feature extraction and Classification
National Institute of Technology Goa, September 2023
 Apply filters
 Artefact removal
 Extract time or frequency component as a feature
 Classification
 Prosthetics Control

Data Acquisition
Objective: MI classification using BCI
Recording device: 32 channel dry electrode, 250 Hz sample rate
Brain Vision Analyzer, Brain Vision Recorder
An Intel(R) Core(TM) i7-4790 processor with 3.60 GHz and 8 GB of RAM .
Dataset :
features quantity
subjects 10(7 male,3 female)(18- 28 yrs)
electrodes 3 EEG channels(c3, cz, c4)
sessions 2(one for each class)
trials 60(30 for each class)
classes 2(open,close)

Dataset description
0 1 2 3 4 5 6 7 8 9 10 11 12 13
Motor Imagery
Blank
Screen
Blank Screen
Visual
Signal
Figure 10: Timing scheme of a trial

Proposed method
Raw EEG signal
Band Pass filter (7-30) Hz
CWT images of EEG
data
Figure:11 Generation of MI-EEG image from Continuous Wavelet Transform
Continuous Wavelet
Transform

Proposed method
Figure 12: Scalograms for two different labels, 'CLOSE' and 'OPEN', using the Continuous Wavelet Transform (CWT)

Proposed method
Figure 14: Schematic overview of motor imagery classification using Attention based CNN-BiLSTM model

Results
Subjects Accuracy (%)
Subject 1 90
Subject 2 71
Subject 3 67
Subject 4 55
Subject 5 73
Subject 6 69
Subject 7 75
Subject 8 84
Subject 9 82
Subject 10 61
Parameters
Input size- (1500, 40,3)
No. Of conv layers-4
Activation function Leaky Relu
Activation function (o/p)- Sigmoid
Optimizers- Adam
Learning rate- 0.001
Drop out rate- 0.4

References
1. Rao, R.P.: Brain-computer interfacing: an introduction. Cambridge University Press (2013)
2. Kumar, S., Rajshekher, G., Prabhakar, S., et al.: Positron emission tomography in neurological diseases.
Neurology India 53(2), 149 (2005)
3. Bablani, A., Edla, D.R., Tripathi, D., Cheruku, R.: Survey on brain-computer interface: An emerging
computational intelligence paradigm. ACM Computing Surveys (CSUR) 52(1), 1–32 (2019).
4. F. Cincotti, D. Mattia, F. Aloise, S. Bufalari, G. Schalk, G. Oriolo, A. Cherubini, M. G. Marciani, F. Babiloni,
Non-invasive brain–computer interface system: towards its application as assistive technology, Brain
research bulletin 75 (6) (2008) 796–803.
5. E. M. Schmidt, Single neuron recording from motor cortex as a possible source of signals for control of
external devices, Annals of biomedical engineering 8 (4) (1980) 339–349.
6. N. Veena, N. Anitha, A review of non-invasive bci devices, Int. J. Biomed. Eng. Technol 34 (3) (2020) 205–233.

Brain computer interface using machine learning

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