Brain computer interface using machine learning

bci

1. 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

2. National Institute of Technology, Patna , 22 March 2021 Introduction National Institute of Technology Goa, January 2024 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.

3. National Institute of Technology, Patna , 22 March 2021 Basic components of BCI National Institute of Technology Goa , January 2024 Figure 2: Components of Brain Computer Interfaces

4. Types of BCI Invasive Multielectrode array of tens to hundreds of electrodes implanted into brain cortical tissue from which “movement intent” is decoded. T hey allow recording of action potentials (the acknowledged output signals of neurons) at the millisecond timescale. Greater spatial resolution. National Institute of Technology Goa, January 2024

5. National Institute of Technology, Patna , 22 March 2021 Invasive National Institute of Technology Goa, January 2024 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 i s placed under the dura onto the brain surface (B) X-ray image of the skull showing the location of the electrode array. A B

6. Types of BCI 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 National Institute of Technology Goa, January 2024

7. 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 National Institute of Technology Goa, January 2024

8. 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. f MRI 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 National Institute of Technology Goa, January 2024

9. National Institute of Technology, Patna , 22 March 2021 Non-invasive National Institute of Technology Goa, January 2024 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.

10. National Institute of Technology, Patna , 22 March 2021 Applications of BCI National Institute of Technology Goa, January 2024 R eplace 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 I ndustrial robotics, increasing worker safety by keeping people away from potentially demanding jobs M ake games more user-friendly

11. National Institute of Technology, Patna , 22 March 2021 Motor Imagery National Institute of Technology Goa, January 2024 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

12. National Institute of Technology, Patna , 22 March 2021 Literature Survey Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks National Institute of Technology Goa, January 2024 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 networks 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

13. National Institute of Technology, Patna , 22 March 2021 Literature Survey National Institute of Technology Goa, January 2024 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 Signals 5 Alwasiti et al.” Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation ”, IEEE 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

14. National Institute of Technology, Patna , 22 March 2021 Signal acquisition National Institute of Technology Goa, January 2024 1. Collect raw EEG data Figure 8: Raw EEG data

15. National Institute of Technology, Patna , 22 March 2021 Channel National Institute of Technology Goa, January 2024 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

16. National Institute of Technology, Patna , 22 March 2021 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

17. National Institute of Technology, Patna , 22 March 2021 Data Acquisition National Institute of Technology Goa, January 2024 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 )

18. National Institute of Technology, Patna , 22 March 2021 Dataset description National Institute of Technology Goa, January 2024 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

19. National Institute of Technology, Patna , 22 March 2021 Proposed method National Institute of Technology Goa, January 2024 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

20. National Institute of Technology, Patna , 22 March 2021 Proposed method National Institute of Technology Goa, January 2024 Figure 12: Scalograms for two different labels, 'CLOSE' and 'OPEN', using the Continuous Wavelet Transform (CWT)

21. Proposed method National Institute of Technology Goa, January 2024 Figure 14: Schematic overview of motor imagery classification using Attention based CNN- BiLSTM model

22. National Institute of Technology, Patna , 22 March 2021 Results National Institute of Technology Goa, January 2024 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

23. National Institute of Technology, Patna , 22 March 2021 References National Institute of Technology Goa, January 2024 Rao , R.P.: Brain-computer interfacing: an introduction. Cambridge University Press (2013) Kumar, S., Rajshekher , G., Prabhakar , S., et al.: Positron emission tomography in neurological diseases. Neurology India 53(2), 149 (2005) 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). 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. 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.

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