Quantifying Electrode Reliability During Brain–Computer Interface Operation

One of the problems of noninvasive brain-computer interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator's attention since raw signals are not usually continuously visualized and monitored during BCI-actuated device operation. Anomalous data can for instance be the result of electrode misplacement, degrading impedance or loss of connectivity. Since this problem can develop at run time, there is a need of a systematic approach to evaluate electrode reliability during online BCI operation. In this paper, we propose two metrics detecting how much each channel is deviating from its expected behavior. This quantifies electrode reliability at run time which could be embedded into BCI data processing to increase performance. We assess the effectiveness of these metrics in quantifying signal degradation by conducting three experiments: Electrode swap, electrode manipulation, and offline artificially degradation of P300 signals.

I. Introduction

Brain–computer interfaces (BCIs) provide the possibility of a direct, nonmuscular communication, and control channel by recognizing patterns of brain activity [1]. The most common recording technique used for these devices is the electroencephalography (EEG). This is due to its high temporal resolution, portability, and relative low cost [2]. However, this technique is characterized by low spatial resolution, since the neural activity is propagated through the brain tissue and scalp which acts as a low-pass filter and smears the activity [3]. Moreover, it is prone to contamination due to muscular artifacts and electromagnetic noise, resulting in a low signal-to-noise ratio (SNR). Furthermore, modifications in the recording settings, e.g., changes in conductivity [4], result in signal variations that may also affect the decoding performance.