Deep Learning Decoding of Mental State in Non-invasive Brain Computer Interface

TL;DR

This paper introduces a novel deep learning approach using a 1D convolutional neural network with a ResNet-like structure to improve mental state prediction accuracy from EEG data, achieving state-of-the-art results.

Contribution

The paper presents a new deep learning model that enhances EEG-based mental state decoding accuracy and generality, outperforming traditional methods and existing neural network structures.

Findings

Achieved 96.40% prediction accuracy.

Outperformed traditional KNN and SVM methods.

Set new state-of-the-art results on a mental state dataset.

Abstract

Brain computer interface (BCI) has been popular as a key approach to monitor our brains recent year. Mental states monitoring is one of the most important BCI applications and becomes increasingly accessible. However, the mental state prediction accuracy and generality through encephalogram (EEG) are not good enough for everyday use. Here in this paper we present a deep learning-based EEG decoding method to read mental states. We propose a novel 1D convolutional neural network with different filter lengths to capture different frequency bands information. To improve the prediction accuracy, we also used a resnet-like structure to train a relatively deep convolutional neural network to promote feature extraction. Compared with traditional ways of predictions such as KNN and SVM, we achieved a significantly better result with an accuracy of 96.40%. Also, in contrast with some already published open source deep neural network structures, our methods achieved the state of art prediction accuracy on a mental state recognition dataset. Our results demonstrate using only 1D convolution could extract the features of EEG and the possibility of mental state prediction using portable EEG devices.