,Table of Contents
Cover
Title page
Copyright
Contributors
1: Multiclass sleep stage classification using artificial intelligence based time-frequency
distribution and CNN
Abstract
1.1: Introduction
1.2: Materials and methods
1.3: Results
1.4: Discussion
1.5: Conclusions
References
2: A comprehensive review of the movement imaginary brain-computer interface methods:
Challenges and future directions
Abstract
2.1: Introduction
2.2: PRISMA guideline
2.3: Results
2.4: Discussion
2.5: Conclusion and future scope
,References
3: A new approach to feature extraction in MI-based BCI systems
Abstract
3.1: Introduction
3.2: Types and applications
3.3: BSS and its application in BCI
3.4: Related work
3.5: Proposed method
3.6: Computer simulation and result
3.7: Discussion
3.8: Conclusion
References
4: Evaluation of power spectral and machine learning techniques for the development of subject-
specific BCI
Abstract
4.1: Introduction
4.2: Materials
4.3: Methods
4.4: Performance verification
4.5: Parameters selection
4.6: Results
4.7: Discussions
4.8: Conclusion
Conflicts of interest
, References
5: Concept of AI for acquisition and modeling of noninvasive modalities for BCI
Abstract
5.1: Introduction
5.2: Electroencephalogram
5.3: Artificial intelligence for signal analysis
5.4: Communication interface between brain and machine
5.5: Methodology
5.6: Results
5.7: Discussion and future scope
5.8: Conclusion
References
6: Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals
Abstract
6.1: Introduction
6.2: Methods and materials
6.3: Results and discussion
6.4: Conclusions and recommendations
References
7: Detection of epileptic seizure disorder using EEG signals
Abstract
7.1: Introduction
7.2: Background on EEG signals
Cover
Title page
Copyright
Contributors
1: Multiclass sleep stage classification using artificial intelligence based time-frequency
distribution and CNN
Abstract
1.1: Introduction
1.2: Materials and methods
1.3: Results
1.4: Discussion
1.5: Conclusions
References
2: A comprehensive review of the movement imaginary brain-computer interface methods:
Challenges and future directions
Abstract
2.1: Introduction
2.2: PRISMA guideline
2.3: Results
2.4: Discussion
2.5: Conclusion and future scope
,References
3: A new approach to feature extraction in MI-based BCI systems
Abstract
3.1: Introduction
3.2: Types and applications
3.3: BSS and its application in BCI
3.4: Related work
3.5: Proposed method
3.6: Computer simulation and result
3.7: Discussion
3.8: Conclusion
References
4: Evaluation of power spectral and machine learning techniques for the development of subject-
specific BCI
Abstract
4.1: Introduction
4.2: Materials
4.3: Methods
4.4: Performance verification
4.5: Parameters selection
4.6: Results
4.7: Discussions
4.8: Conclusion
Conflicts of interest
, References
5: Concept of AI for acquisition and modeling of noninvasive modalities for BCI
Abstract
5.1: Introduction
5.2: Electroencephalogram
5.3: Artificial intelligence for signal analysis
5.4: Communication interface between brain and machine
5.5: Methodology
5.6: Results
5.7: Discussion and future scope
5.8: Conclusion
References
6: Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals
Abstract
6.1: Introduction
6.2: Methods and materials
6.3: Results and discussion
6.4: Conclusions and recommendations
References
7: Detection of epileptic seizure disorder using EEG signals
Abstract
7.1: Introduction
7.2: Background on EEG signals
