Trends in Compressive Sensing for EEG Signal Processing Applications

Area of Science:

• Neural Engineering
• Neuroscience
• Signal Processing

Background:

• Big data in neural engineering aids understanding of brain disorders and rehabilitation.
• Compressive Sensing (CS) integration with neural engineering addresses large neurological datasets for efficiency.
• Electroencephalography (EEG) signals are crucial for brain-computer interfaces (BCIs) with diverse applications.

Purpose of the Study:

• To review EEG-based approaches utilizing CS for fast and energy-saving solutions.
• To examine current practices, opportunities, and challenges of CS in BCIs.
• To summarize CS reconstruction algorithms, sparse bases, and measurement matrices for EEG signal processing.

Main Methods:

• Literature review focusing on CS applications in EEG-based BCIs.
• Analysis of major CS reconstruction algorithms, sparse bases, and measurement matrices.
• Overview of reconstruction-free CS approaches in the BCI field.

Main Results:

• CS offers significant advantages for fast and energy-efficient EEG processing in BCIs.
• The choice of reconstruction algorithm, sparse basis, and measurement matrix impacts CS-based EEG study performance.
• Reconstruction-free CS methods present an alternative approach for BCI applications.

Conclusions:

• Optimizing CS parameters is crucial for enhancing the performance of EEG-based BCIs.
• Further research into CS integration can unlock new opportunities and address challenges in BCI development.
• CS framework holds significant potential for advancing BCI applications in neural engineering.