Analysis of Prefrontal Single-Channel EEG Data for Portable Auditory ERP-Based Brain–Computer Interfaces

Abstract

An electroencephalogram (EEG)-based brain-computer interface (BCI) is a tool to non-invasively control computers by translating the electrical activity of the brain. This technology has the potential to provide patients who have severe generalized myopathy, such as those suffering from amyotrophic lateral sclerosis (ALS), with the ability to communicate. Recently, auditory oddball paradigms have been developed to implement more practical event-related potential (ERP)-based BCIs because they can operate without ocular activities. These paradigms generally make use of clinical (over 16-channel) EEG devices and natural sound stimuli to maintain the user's motivation during the BCI operation; however, most ALS patients who have taken part in auditory ERP-based BCIs tend to complain about the following factors: (i) total device cost and (ii) setup time. The development of a portable auditory ERP-based BCI could overcome considerable obstacles that prevent the use of this technology in communication in everyday life. To address this issue, we analyzed prefrontal single-channel EEG data acquired from a consumer-grade single-channel EEG device using a natural sound-based auditory oddball paradigm. In our experiments, EEG data was gathered from nine healthy subjects and one ALS patient. The performance of auditory ERP-based BCI was quantified under an offline condition and two online conditions. The offline analysis indicated that our paradigm maintained a high level of detection accuracy (%) and ITR (bits/min) across all subjects through a cross-validation procedure (for five commands: 70.0 ± 16.1 and 1.29 ± 0.93, for four commands: 73.8 ± 14.2 and 1.16 ± 0.78, for three commands: 78.7 ± 11.8 and 0.95 ± 0.61, and for two commands: 85.7 ± 8.6 and 0.63 ± 0.38). Furthermore, the first online analysis demonstrated that our paradigm also achieved high performance for new data in an online data acquisition stream (for three commands: 80.0 ± 19.4 and 1.16 ± 0.83). The second online analysis measured online performances on the different day of offline and first online analyses on a different day (for three commands: 62.5 ± 14.3 and 0.43 ± 0.36). These results indicate that prefrontal single-channel EEGs have the potential to contribute to the development of a user-friendly portable auditory ERP-based BCI.

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

“…However, the setup process is tedious (attaching the electrodes, adjusting the impedance) and participants find the system uncomfortable. In recent years, research has been conducted into use of monochannel EEG systems in brain-computer interfaces (BCI´s) [30] (analyzed band: 0.5-10 Hz), [31] (steady-state visual evoked potentials), sleep studies [32,33], etc. However, to our knowledge, a single-channel system for detecting activity in the gamma band has not been implemented in imaginary movements.…”

A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

“…Both the large number of sensors used in BCI helmets and the annoyance caused by the saline solution make these devices uncomfortable to be used in real world applications. For this reason, recently several research activities are focusing on innovative BCI devices based on few dry electrodes [3] , [4] , [5] , [6] . As a consequence, there is a strong need to develop and publish new BCI datasets where aforementioned devices are used to capture brain signals.…”

Single Channel EEG Classification: A Case Study on Prediction of Major Depressive Disorder Treatment Outcome

“…These electrodes are also connected to some electrical devices (e.g., amplifiers) with many wires. All of these accessories and even only attaching several electrodes on the head (in case of wireless EEG) can be annoying for patients, troublesome for operators [1]. Besides the mentioned constraints, multichannel EEGs are limited to use in the scientific and clinical labs, and it can make infeasible the variety of continuous and real-time EEG based applications like online detection of epileptic seizures [2].…”