Wearable mental-health monitoring platform with independent component analysis and nonlinear chaotic analysis

Abstract

The wearable mental-health monitoring platform is proposed for mobile mental healthcare system. The platform is headband type of 50 g and consumes 1.1 mW. For the mental health monitoring two specific functions (independent component analysis (ICA) and nonlinear chaotic analysis (NCA)) are implemented into CMOS integrated circuits. ICA extracts heart rate variability (HRV) from EEG, and then NCA extracts the largest lyapunov exponent (LLE) as physiological marker to identify mental stress and states. The extracted HRV is only 1.84% different from the HRV obtained by simple ECG measurement system. With the help of EEG signals, the proposed headband mental monitoring system shows 90% confidence level in stress test, which is better than the test results of only HRV.