Brain-computer interaction

Abstract

This paper is a short introduction to a special ICMI session on brain-computer interaction. During this paper, we first discuss problems, solutions, and a five-year view for brain-computer interaction. We then talk further about unique issues with multimodal and hybrid brain-computer interfaces, which could help address many current challenges. This paper presents some potentially controversial views, which will hopefully inspire discussion about the different views on brain-computer interfacing, how to embed brain-computer interfacing in a multimodal and multi-party context, and, more generally, how to look at brain-computer interfacing from an ambient intelligence point of view.

Neurofeedback and the Aging Brain: A Systematic Review of Training Protocols for Dementia and Mild Cognitive Impairment

“…On a positive note, neurofeedback protocols are rarely associated with side effects (Hawkinson et al, 2012). Potential side effects experienced by patients include potential physical discomfort experienced before [e.g., during EEG cap preparation and calibration (Nijholt et al, 2011)] or during training sessions [e.g., claustrophobia due to the physical restriction in fMRI scanners (Sulzer et al, 2013)]. Noteworthy, only one study reported withdraws before the primary endpoint (Marlats et al, 2020), one other study reported drop-offs before the followup completion (Lavy et al, 2019), and three studies reported data exclusion due to technical problems or excessive noise in the recordings (Hohenfeld et al, 2017(Hohenfeld et al, , 2020Jang et al, 2019).…”

Using Fixation-Related Potentials for Inspecting Natural Interactions

“…Some of the more commonly used signals include Electromyography (EMG) as well as Galvanic Skin Response (GSR) (Müller-Putz et al, 2015). These modalities have shown to reliably improve predictive accuracy of BCIs (Nijholt et al, 2011), but still only provide general insights into the users state. They fail to supply precise and momentary information about any performed action.…”

An online hybrid BCI system based on SSVEP and EMG

“…P300-Motor Imagery hybrid BCI was another combination that allowed investigators to build both discrete and asynchronous control for more complicated and practical applications (Rebsamen et al 2008). A mixed hybrid BCI is a device where EEG and information from residual functionalities of disabled people have been merged to maximize their communication abilities (Nijholt et al 2011, Amiri et al 2013, Lalitharatne et al 2013. An EMG based hybrid BCI was developed in different shapes such as EMG-Motor Imagery hybrid BCI (Leeb et al 2011, Perdikis et al 2014 and EMG-P300 hybrid BCI (Holz et al 2013).…”