Sensing Brain Activity Using EEG and Machine Learning

Dear Colleagues,

Understanding brain activity is challenging due to its high structural and functional complexity, as well as high inter- and intra-subject variability. One of the most promising approaches to sense and study it is in the spatiotemporal domain using electroencephalography (EEG) and machine learning techniques (ML). The applied ML techniques address the specifics of EEG data and sensed neural processes, including noise, artefacts, volume conduction, brain connectivity, limited spatial resolution, and high temporal resolution. This Special Issue aims to collect papers presenting recent research on brain activity sensing, analysis, and recognition using machine learning techniques on EEG data, including but not limited to:

• Feature-based ML approaches;
• Artificial neural network architectures;
• Reinforcement learning;
• System dynamics analysis;
• Statistical approaches in modelling;
• Applications of graph theory.

And various applications of machine learning to EEG analysis, such as:

• Clinical diagnostics;
• Emotion recognition;
• Attention recognition;
• Brain activity classification;
• Brain–computer interfaces (BCI);
• Artefact removal;
• Brain connectivity analysis.

Dr. Peter Rogelj
Guest Editor

Manuscript Submission Information

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