Brain-Computer Interfaces

<p>1. Advances in Human Activity Recognition: Harnessing Machine Learning And Deep Learning With Topological Data Analysis<br>2. Design And Validation Of A Hybrid Programmable Platform For The Acquisition Of Exg Signals<br>3. FBSE Based Automated Classification of Motor Imagery EEG Signals in Brain-Computer Interface<br>4. Automated Detection Of Brain Disease Using Quantum Machine Learning<br>5. A Study Of The Relationship Of Wavelet Transform Parameters And Their Impact On Eeg Classification Performance<br>6. Bcis For Stroke Rehabilitation<br>7. Decoding Imagined Speech For Eeg-Based Bci<br>8. A Comparison Of Deep Learning Methods And Conventional Methods For Classification Of Ssvep Signals In Brain Computer Interface Framework<br>9. Benchmarking Convolutional Neural Networks On Continuous Eeg Signals: The Case Of Motor Imagery-Based Bci<br>10. Advancements in The Diagnosis Of Alzheimer&rsquo;S Disease (Ad) Through Biomarker Detection<br>11. Alcoholism Identification By Processing The Eeg Signals Using Oscillatory Modes Decomposition And Machine Learning<br>12. Investigating the role of cortical rhythms in modulating kinematic synergies and exploring their potential for stroke rehabilitation<br>13. Stimulus-Independent Non-Invasive Bci Based On Eeg Patterns Of Inner Speech<br>14. A Review of Modern Brain Computer Interface Investigations And Limits<br>15. Non-Invasive Brain-Computer Interfaces Using Fnirs, Eeg And Hybrid Fnirs/Eeg<br>16. Eeg-Based Cognitive Fatigue Recognition Via Machine Learning and Analysis Of Multidomain Features<br>17. Passive Brain-Computer Interfaces for Cognitive and Pathological Brain Physiological States Monitoring And Control<br>18. Beyond Brainwaves: Recommendations for Integrating Robotics & Virtual Reality for Eeg-Driven Brain-Computer Interface<br>19. A Sociotechnical Systems Perspective To Support Brain-Computer Interface Development<br>20. Assessing Systemic Benefit and Risk in The Development Of Bci Neurotechnology<br>21. Recent Development of Single Channel EEG-Based Automated Sleep Stage Classification: Review And Future Perspectives</p>