Comprehensive Summary
This literature review, presented by Zhang et al., reviews the progress in dry electrode electroencephalography (EEG) development for brain-computer interface (BCI) applications by summarizing hardware and signal post-processing techniques. The paper categorizes electrode advancements into microelectromechanical system (MEMS) dry electrodes, non-contact dry electrodes, and dry contact electrodes. The study then examines the applications of dry electrode EEG in emotion recognition, fatigue/drowsiness detection, motor imagery, and steady state visual evoked potentials. The study determines that deep learning models have significantly improved signal post-processing with emotion recognition studies achieving accuracies as high as 99% and drowsiness detection systems demonstrating accuracies above 95%. The study also identifies flexible textile-based electrodes and carbon nanotube (CNT) paired with adhesive PDMS composite as some of the key developments in dry electrode efficacy. Despite these advances, there are still challenges in lowering electrode-skin impedance and the authors identify establishing standardized evaluation benchmarks as one of the key factors in making significant progress.
Outcomes and Implications
Dry electrodes do not require conductive gel and provide more comfort, portability, and usability in comparison to traditional wet electrodes. Research into improving the quality and comfort of dry electrodes would allow users to experience all the recording and accuracy benefits of wet electrodes while also increasing user accessibility and comfort, thus making the BCI systems more practical for use. Dry electrode EEG systems are used diagnostically for epilepsy and sleep disorders. Involving the usage of AI and deep learning models for signal post-processing would also be useful in improving clinical diagnostics of epilepsy and sleep disorders. The application of motor imagery could be used to improve assistive devices for patients with stroke or paralysis. Both the hardware and signal post-processing developments in dry contact electrodes can be used to improve diagnostic care and the development of assistive medical devices.