Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing.

Benjamin Wittevrongel,Marc M Van Hulle

doi:10.3389/fnins.2017.00630

Abstract

Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a direct communication channel with an external device. Albeit they have been hailed to (re-)establish communication in persons suffering from severe motor- and/or communication disabilities, only recently BCI applications have been challenging other assistive technologies. Owing to their considerably increased performance and the advent of affordable technological solutions, BCI technology is expected to trigger a paradigm shift not only in assistive technology but also in the way we will interface with technology. However, the flipside of the quest for accuracy and speed is most evident in EEG-based visual BCI where it has led to a gamut of increasingly complex classifiers, tailored to the needs of specific stimulation paradigms and use contexts. In this contribution, we argue that spatiotemporal beamforming can serve several synchronous visual BCI paradigms. We demonstrate this for three popular visual paradigms even without attempting to optimizing their electrode sets. For each selectable target, a spatiotemporal beamformer is applied to assess whether the corresponding signal-of-interest is present in the preprocessed multichannel EEG signals. The target with the highest beamformer output is then selected by the decoder (maximum selection). In addition to this simple selection rule, we also investigated whether interactions between beamformer outputs could be employed to increase accuracy by combining the outputs for all targets into a feature vector and applying three common classification algorithms. The results show that the accuracy of spatiotemporal beamforming with maximum selection is at par with that of the classification algorithms and interactions between beamformer outputs do not further improve that accuracy.

Highlights

In a Brain-Computer Interface (BCI), signals are recorded from the brain with the aim to enable users to interact with an external device, without the need for muscular, vocal or other means of communication (Vidal, 1973)
For all stimulus repetitions in the P300 case, there is no significant difference between maximal beamformer output prediction and NN- or SVMbased classification
Note that the interquartile ranges are considerably smaller compared to the P300 paradigm, indicating a higher consistency across subjects, which may be due to the fact that SSVEP is an automatic visual response while P300 is a cognitive potential and more influenced by ongoing mental activity

Summary

Introduction

In a Brain-Computer Interface (BCI), signals are recorded from the brain with the aim to enable users to interact with an external device, without the need for muscular, vocal or other means of communication (Vidal, 1973). Among the most performant BCI paradigms, in terms of accuracy and speed, are the ones where targets are visually stimulated and the user directs his/her gaze to target to be selected (for review, see Bin et al, 2009; Nicolas-Alonso and Gomez-Gil, 2012). One popular paradigm is based on the P300 event-related potential (ERP), an EEG component recorded over the centroparietal region that exhibits a positive deflection in amplitude, peaking around 300 ms (whence its name), in sync with the onset of an infrequent stimulus (called oddball) to which the subject pays attention. In order to address the relatively low signal-to-noise ratio (SNR), several oddball events need to be generated and epochs averaged before P300 responses can be detected in them, resulting in a reduction in communication speed. The recently introduced spatiotemporal beamforming filter has been shown to be at par with an optimized linear SVM for P300 detection (Wittevrongel and Van Hulle, 2016a)

Methods

Results

Discussion

Conclusion