Sabotage Detection Using DL Models on EEG Data From a Cognitive-Motor Integration Task

In this study, we have proposed a novel method to detect the difference in cortical activity between best effort (no-sabotage) and willful under-performance (sabotage) using a deep learning (DL) approach on the electroencephalogram (EEG) signals. The EEG signals from a wearable four-channel headband were acquired during a CMI task. Each participant completed sabotage and no-sabotage conditions in random order. A multi-channel convolutional neural network with long short term memory (CNN-LSTM) model with self-attention has been used to perform the time-series classification into sabotage and no-sabotage, by transforming the time-series into two-dimensional (2D) image-based scalogram representations. This approach allows the inspection of frequency-based, and temporal features of EEG, and the use of a multi-channel model facilitates in capturing correlation and causality between different EEG channels. By treating the 2D scalogram as an image, we show that the trained CNN-LSTM classifier based on automated visual analysis can achieve high levels of discrimination and an overall accuracy of 98.71% in case of intra-subject classification, as well as low false-positive rates. The average intra-subject accuracy obtained was 92.8%, and the average inter-subject accuracy was 86.15%. These results indicate that our proposed model performed well on the data of all subjects. We also compare the scalogram-based results with the results that we obtained by using raw time-series, showing t...
Source: Frontiers in Human Neuroscience - Category: Neuroscience Source Type: research