A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study.

Weichen Huang,Tianyou Yu,Peiqi Zhang,Yuanqing Li,Qiang Guo,Zhenghui Gu

doi:10.1109/tbme.2020.3047812

Abstract

Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.

Highlights

B RAIN-COMPUTER interfaces (BCIs) provide a feasible approach for humans to interact with computers without the participation of peripheral nerves and muscles [1]
The results show that our system demonstrates an equivalent performance in terms of both spelling accuracy and information transform rate (ITR) compared with some benchmark EEG-based speller systems, and it achieves a substantial improvement in the ITR compared with the previous SEEG-based BCI system
The contacts in the fusiform gyrus (FG) achieved excellent performance in our analysis. These findings reveal that the electrode distribution is important in the performance of SEEG-based BCI systems

Summary

Introduction

B RAIN-COMPUTER interfaces (BCIs) provide a feasible approach for humans to interact with computers without the participation of peripheral nerves and muscles [1]. Eventrelated potentials (ERPs) are common brain patterns used to examine brain activities and cognitive functions in stimulusresponse paradigms and produce control signals in BCIs. P300, one of the ERP components, is evoked by a target oddball stimulus in the auditory, visual, or somatosensory modalities and occurs at a latency of approximately 300 ms, depending on the subject and event-eliciting variables [2]. Diverse types of BCI systems have been developed based on P300, such as word spelling [3], prosthetic device control [4], and cognitive assessment for patients with disorders of consciousness [5]. The most common BCI systems to date are based on electroencephalography (EEG) signals. Many obstacles hinder the development of EEG-based BCIs. EEG signals are highly mixed and noisy, which may affect the performance of BCI systems [7]. EEG cannot record intracranial brain activity and has a limited topographical resolution and frequency range [8], which restricts its use in studies of brain mechanisms

Methods

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Discussion

Conclusion