Gamma oscillation optimally predicts finger movements

Abstract

Our fingers are the most dexterous and complicated parts of our body and play a significant role in our daily activities. Non-invasive techniques, such as Electroencephalography (EEG) and Electromyography (EMG) can be used to collect neural and muscular signals related to finger movements. In this study, we combined an 8-channel EMG and a 31-channel EEG while the human subject moved one of the five fingers on the right hand. To identify the best EEG frequency features that encode distinct finger movements, we systematically examined the decoding accuracies of the slow-cortical potentials and three types of sensorimotor rhythms, namely the Mu, beta, and gamma oscillations. For both EMG and EEG, we came up with a simple and unified root mean square or power approach that avoided the complex signal features used by previous studies. The signal features were then fed into a feedforward artificial-neural-network (ANN) classifier. We found that the low-gamma oscillation provided the best decoding performance over the other frequency bands, ranging from 65.0 % to 89.0 %, which was comparable to the EMG performance. Combining EMG and low gamma into a single ANN can further improve the outcome for subjects who had showed suboptimal performances with EMG or EEG alone. This study provided a simple and efficient algorithm for prosthetics that assist patients with sensorimotor impairments.