Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general

Abstract

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

“…[6] More recent works have included passive BCI systems for entertainment, life-style, and ergonomic applications. [7,8] aBCI, with their focus on affective aspects of HCI, can be used for active communication of emotional states and moods, and for passive sensing of affect to inform machines about the affective states of their users.…”

“…As for BCI in general, [8] approaches of neurophysiologyinformed affect sensing can be categorized in terms of their dependence on user volition and stimulation. In [9] we extend the three-legged categorisation (active, reactive, and passive BCI) of [8] to a two-dimensional classification scheme explicating two axes.…”

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

“…EEG based BCI, such as the P300-based virtual keyboard and the SSVEP (Steady state VEP)-based phonedialing system, are well-known reactive BCIs, which derive their output commands from brain activity in response to external stimulation (Zander and Kothe 2011). In these BCIs, users code their intent into the well-designed stimulus that elicits a particular ERP response and the computer decodes the user's intent from the measured brain response.…”

Combining Brain-Computer Interfaces and Haptics: Detecting Mental Workload to Adapt Haptic Assistance

“…Passive BCI: The aim of a passive BCI is not to allow the user to send explicit commands to an application but to provide information concerning his/her mental state with the purpose of adapting or enhancing the interaction accordingly [5,6]. For instance, passive BCI have been used in the context of videogames to adapt the way the system responds to commands [15] or to adapt the content of the game itself [7].…”

“…BCI can also be used by healthy users, for instance for enhancing interaction with video games [4]. An approach called "passive BCI" aims at using brain activity information to adapt and enhance the current application without the need for the user to voluntarily control his/her brain activity [5,6]. For example, this approach has been used to adapt virtual environments content [7].…”