Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks

TL;DR

This paper introduces a neural network approach for classifying multiple endogenous brain activity paradigms from EEG signals, significantly improving accuracy for brain-machine interfaces in human-robot collaboration.

Contribution

It proposes a novel temporal information-based neural network (TINN) for inter-paradigm EEG classification, achieving higher accuracy than previous methods.

Findings

Achieved 93% classification accuracy for three mental imagery paradigms.

Identified significant neurophysiological features across brain regions.

Demonstrated the feasibility of multi-paradigm EEG classification for BMI.

Abstract

Human-robot collaboration has the potential to maximize the efficiency of the operation of autonomous robots. Brain-machine interface (BMI) would be a desirable technology to collaborate with robots since the intention or state of users can be translated from the neural activities. However, the electroencephalogram (EEG), which is one of the most popularly used non-invasive BMI modalities, has low accuracy and a limited degree of freedom (DoF) due to a low signal-to-noise ratio. Thus, improving the performance of multi-class EEG classification is crucial to develop more flexible BMI-based human-robot collaboration. In this study, we investigated the possibility for inter-paradigm classification of multiple endogenous BMI paradigms, such as motor imagery (MI), visual imagery (VI), and speech imagery (SI), to enhance the limited DoF while maintaining robust accuracy. We conducted the statistical and neurophysiological analyses on MI, VI, and SI and classified three paradigms using the proposed temporal information-based neural network (TINN). We confirmed that statistically significant features could be extracted on different brain regions when classifying three endogenous paradigms. Moreover, our proposed TINN showed the highest accuracy of 0.93 compared to the previous methods for classifying three different types of mental imagery tasks (MI, VI, and SI).