Systematic Review of the Literature on How Machine Learning is used to classify EEG signals/Brainwave forms (Delta, Theta, Alpha, Beta, Gamma)
Organization
Software Engineering and Information Systems
Abstract
The Electroencephalography (EEG) analysis has been an important tool in neuroscience’s applications such as Brain Computer Interface (BCI) and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning (ML) to uncover relevant information for neural classification and neuroimaging.
Recently, the availability of large EEG datasets and advances in ML have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals, and understanding the information it may contain for brain functionality. The robust automatic categorisation of these signals is an important step towards making the use of EEG more practical in many applications.
Towards this goal, a systematic review of the literature on all machine learning algorithms and applications that use EEG classifications needs to be performed to address the following critical questions:
1.\tWhich EEG classification tasks have been explored using machine learning?
2.\tWhat input formulations have been used for training the machine learning algorithms?
3.\tAre there specific machine learning algorithms suitable for specific types of tasks?
4.\tCompare all suitable results on the classification on EEG signals
5.\tFinally, a framework will be proposed based on the systematic review of the literature which serves as a path for the classifications of EEG signals/brain waveforms.
Motivation: In the near future, we envision these techniques to enable early diagnosis systems for the detection of neurodegenerative diseases. We can also use them to show signature patterns in physiological data. This can range from spine injuries to heart disease or cancer. This could even change how we treat early diagnosis.
Some relevant literature:
[1] Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort “DEEP LEARNING-BASED ELECTROENCEPHALOGRAPHY ANALYSIS: A SYSTEMATIC REVIEW”. Jan 2019.
https://arxiv.org/pdf/1901.05498.pdf
[2] Craik A, He Y, Contreras-Vidal JL, “Deep learning for electroencephalogram (EEG) classification tasks: a review”, J Neural Eng. 2019 Jun;16(3) https://www.ncbi.nlm.nih.gov/pubmed/30808014
[3] Laura Dubreuil, “How can we apply AI, Machine Learning or Deep Learning to EEG?”, March 2018 (https://www.neuroelectrics.com/blog/from-ai-to-deep-learning-applied-to-eeg/)
Recently, the availability of large EEG datasets and advances in ML have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals, and understanding the information it may contain for brain functionality. The robust automatic categorisation of these signals is an important step towards making the use of EEG more practical in many applications.
Towards this goal, a systematic review of the literature on all machine learning algorithms and applications that use EEG classifications needs to be performed to address the following critical questions:
1.\tWhich EEG classification tasks have been explored using machine learning?
2.\tWhat input formulations have been used for training the machine learning algorithms?
3.\tAre there specific machine learning algorithms suitable for specific types of tasks?
4.\tCompare all suitable results on the classification on EEG signals
5.\tFinally, a framework will be proposed based on the systematic review of the literature which serves as a path for the classifications of EEG signals/brain waveforms.
Motivation: In the near future, we envision these techniques to enable early diagnosis systems for the detection of neurodegenerative diseases. We can also use them to show signature patterns in physiological data. This can range from spine injuries to heart disease or cancer. This could even change how we treat early diagnosis.
Some relevant literature:
[1] Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort “DEEP LEARNING-BASED ELECTROENCEPHALOGRAPHY ANALYSIS: A SYSTEMATIC REVIEW”. Jan 2019.
https://arxiv.org/pdf/1901.05498.pdf
[2] Craik A, He Y, Contreras-Vidal JL, “Deep learning for electroencephalogram (EEG) classification tasks: a review”, J Neural Eng. 2019 Jun;16(3) https://www.ncbi.nlm.nih.gov/pubmed/30808014
[3] Laura Dubreuil, “How can we apply AI, Machine Learning or Deep Learning to EEG?”, March 2018 (https://www.neuroelectrics.com/blog/from-ai-to-deep-learning-applied-to-eeg/)
Graduation Theses defence year
2019-2020
Supervisor
Yar Muhammad and Faiz Ali Shah
Spoken language (s)
English
Requirements for candidates
Level
Bachelor, Masters
Application of contact
Name
Yar Muhammad
Phone
E-mail