Quantifying Electrode Reliability During Brain–Computer Interface Operation

Abstract

Abstract-One of the problems of non-invasive BrainComputer 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.

A Novel Passive Method for the Assessment of Skin-Electrode Contact Impedance in Intraoperative Neurophysiological Monitoring Systems

“…Figure 7 contain the results obtained after 10 measurements for different values of + R and − R . The black line represents the theoretical value of α and β, calculated following (8) and provided that + R and − R are known. Consistency between experimental and theoretical results can be observed.…”

“…Kappenman and Luck 7 highlight the importance of electrode impedance on data quality for event-related potentials (ERP) and EEG signals. Sagha et al 8 include high electrode impedance as a source of signal degradation for EEG utilized in brain-computer interfaces. In order to avoid these pernicious effects, the literature sets the limit for the impedance value around 5 kΩ for EEG 6,[9][10][11][12] and for evoked potentials 1 .…”

A Compact Review on State-of-the-Art Brain Tumor Classification Models

“…In the human body, one of the most complex organs that work with billions of cells is the brain [82]. The uncontrolled or abnormal growth of cancerous cells in the body is referred to as tumors [66] [67].…”

A Quasi-Wireless Intraoperatory Neurophysiological Monitoring System

“…Moreover, the system presents a minimal user interface with two purposes: on the one hand, a set of switches is utilized to configure each channel in referential (singleended) or differential mode, as well as change the input reference between ground or any other channel. An additional switch permits selecting between normal acquisition mode or impedance measurement mode, needed to insure that excessive contact impedance does not compromise the signal quality [10][11][12]. Incorrect value of contact resistance and configuration status of the analog front-end is indicated visually by means of an array of multicolor LEDs.…”