Design of Energy-Efficient On-Chip EEG Classification and Recording Processors for Wearable Environments

Abstract

Classification of EEG under wearable environment faces many challenges including motion artifact, electrode DC offset, noise and limited available energy source. This paper describes the design consideration of a multi-channel machine-learning based EEG classification and recording processors for wearable form-factor sensors. The goal is to optimize the detection performance while balancing the analog and digital signal processing to optimize its energy consumption. On-chip classification significantly helps achieving energy-efficiency by reducing the communication overhead of the data. With epileptic seizure detection and recording system examples, we start from choosing number of channels, the sampling rate, and how to effectively extract features out of the down-sampled data. After that, classification algorithms are also discussed in detail. When verified with the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) EEG database, based on Repeated Random Sub-Sampling validation, the seizure detection sensitivity and specificity of the Non-Linear SVM are improved by 12.4% P and 3.56% P, respectively, compared to the Linear-SVM. The LSVM and NLSVM processors are fabricated in 0.18 mu m 1P6M CMOS and consume 1.52 mu J/classification and 1.34 mu J/classification, respectively. Finally, the on-chip memory requirements for storing the raw seizure data is discussed.