EEG Measurement Using Dry Electrodes Comprising Twolayered Conductive Silicone with Different Carbon Concentrations * Electroencephalography (EEG) typically involves the use of wet electrodes, which are inconvenient and impair signal quality. In this study, we propose a new electrode comprising twolayered conductive silicone with different carbon concentrations as a nonmetallic flexible EEG electrode. The proposed electrode was shown to function as an EEG electrode while maintaining its superior flexibility compared to nonmetallic dry electrodes, such as carboncontaining polymer electrodes. Moreover, we hypothesized that the signal quality varies with carbon concentration and contact area, and thus compared the signal correlation coefficient and SNR with that of the wet electrode for 20 sets of electrodes: 15 sets with the carbon:silicone weight ratio ranging from 1.7% to 3.1%, and 5 sets with the electrode diameter in the range 8xe2x80x9318 mm. We observed that the signal quality of the proposed electrode was closest to that of the wet electrode when the carbon:silicone weight ratio was 2.6%xe2x80x932.8% and the diameter was 8xe2x80x9310 mm. Our results suggest that the proposed electrode is a viable gelfree nonmetallic flexible electrode and is suitable for daily use.

Analysis of electroencephalography alteration during sustained cycling exercise using power spectrum and fuzzy entropy Electroencephalogram (EEG) is a common tool to study the changes in brain activity during exercise. In this study, we used recorded Electrocardiography (ECG) to define an averagetomaximal heart rate ratio (AMHRR) as a gauge of relative exercise workload, and explored how the increase of AMHRR affected the brain activity. The EEG signals were recorded from fortyfour healthy subjects on four scalp sites, including C1, C2, P1, and P2 during nine minutes nonstop cycling exercise. The relationships between AMHRR and EEG power spectral or EEG fuzzy entropy (FuzzyEn) were established. Results of EEG power spectral and FuzzyEn displayed similar increasing patterns with AMHRR at all electrodes. Compared with EEG power spectral, the FuzzyEn has better specificity in selecting effective frequency bands, namely theta, alpha and beta bands. The FuzzyEn method can be applied in a wearable device for human machine interface (HMI) in monitoring the EEG during exercise in the future.