Brain-Computer Interfaces: Beyond Medical Applications

Abstract

Brain-Computer Interfaces (BCIs) carry the promise of natural and intuitive human-computer interaction. BCI technology has matured to the extent that it is available for home use. While most BCI technology was developed for medical applications, we identify 7 non-medical applications including device control, user state monitoring and gaming. We rate these on amongst others societal impact and time to market. Breakthroughs are required in the areas of usability, hardware and software, and system integration, but for successful development should also take user characteristics and acceptance into account. We discuss areas of concern like the lack of standardization and provide 10 recommendations to push the field forward.

The effect of multimodal and enriched feedback on SMR-BCI performance

“…This presentation can be inaccurate, because often the input signal contains a degree of uncertainty that can make a precise classification difficult (Hattie and Timperley, 2007;Shute, 2008, van Beers et al, 2002. The crucial step is to extract robustly the relevant information from EEG signals in the presence of various noise sources, signal non-stationarity and with limited amount of data available (McFarland and Wolpaw, 2011;van Erp et al, 2012) and to give meaningful and precise feedback (Hattie and Timperley, 2007;Shute, 2008). Uncertainty is not static and can vary substantially over time.…”

Inductive Means and Sequences Applied to Online Classification of EEG

“…With electroencephalography (EEG), the brain signal is recorded at the surface of the head (on the scalp), offering a simple setup that does not require surgery as it is the case for invasive recording methods. The signal quality of EEG is lower than with invasive methods and the recording is very sensitive to noise, nonetheless possible applications offer promising results [11]. As technologies and signal processing techniques are more and more mature, out-of-the-lab applications and commercial systems are the focus of growing interests [3].…”

Sequential Detection of P300 Waves for High-Throughput Brain-Computer Interfaces

“…The P300 wave has been widely used for brain-computer interfaces (BCIs) [3,4]. For example, the P300 speller [5] is designed to infer a user's intention solely from brain signals and is a valuable tool for individuals with physical disabilities [6][7][8].…”