Towards a Cure for BCI Illiteracy

By Carmen Vidaurre, Benjamin Blankertz. Brain Topography, 2009

514 citations recorded by the source corpus.

Brain–Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research is to understand and solve the problem of “BCI Illiteracy”, which is that BCI control does not work for a non-negligible portion of users (estimated 15 to 30%). Here, we investigate the illiteracy problem in BCI systems which are based on the modulation of sensorimotor rhythms. In this paper, a sophisticated adaptation scheme is presented which guides the user from an initial subject-independent classifier that operates on simple features to a subject-optimized state-of-the-art classifier within one session while the user interacts the whole time with the same feedback application. While initial runs use supervised adaptation methods for robust co-adaptive learning of user and machine, final runs use unsupervised adaptation and therefore provide an unbiased measure of BCI performance. Using this approach, which does not involve any offline calibration measurement, good performance was obtained by good BCI participants (also one novice) after 3–6 min of adaptation. More importantly, the use of machine learning techniques allowed users who were unable to achieve successful feedback before to gain significant control over the BCI system. In particular, one participant had no peak of the sensory motor idle rhythm in the beginning of the experiment, but could develop such peak during the course of the session (and use voluntary modulation of its amplitude to control the feedback application).

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/99f486ae58cfb29f48ce0a5c07b34e2c9f37f6f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Towards the utilization of EEG as a brain imaging tool (2012, methodology): “…EEG domain they are mainly used in Brain–Computer Interface (BCI) applications (e.g. Bianchi et al., 2010; Rivet et al., 2011; Sannelli et al., 2010; Vidaurre and Blankertz, 2009) where the research objectives are often focused on obtaining a differential metric of brain activity (e.g. a metric…”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “A recent study with healthy volunteers [193] who either had no experience or had not been able to control a BCI with sufﬁcient level of control for a communication application (70 % of accuracy in a two class system) has shown the advantage of this approach.”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, methodology): “An adaptive machine learning approach incorporating neurophysiological and psychological traits has been proposed to reduce BCI illiteracy [60].”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, unknown): “Previous studies have primarily reported the problem of BCI illiteracy with respect to the MI paradigm [21,34,35] or, when examined across multiple paradigms, only with small subject groups (less than five) [36].”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, methodology): “…some research groups claim that it is possible to do this in an unsupervised manner (Li and Guan, 2006; Blumberg et al., 2007), all online studies published so far use supervised adaptation method for the initial period (Vidaurre et al., 2006, 2007; Wang et al., 2007; Vidaurre and Blankertz, 2010).”
Single-trial analysis and classification of ERP components - A tutorial (2011, unknown): “, 2001), which finally also attracted the machine learning community to the field (Blankertz et al., 2002; Vidaurre and Blankertz, 2010; Hong et al., 2009; Müller et al., 2008; Blankertz et al., 2008b, 2007a, 2006; Parra et al., 2008, 2003; Wang et al., 2004; Tomioka and Müller, 2010).”
Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention (2011, background): “However, motor imagery paradigms face the problem that a subset of participants does not obtain significant BCI control, a problem that is only partially solved [9-11].”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, background): “Illiteracy occurs when a user is unable to control a BCI because they do not manage to produce the high-quality brain signals required [177,187].”
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021, background): “For example, under the sensory motor rhythm (SMR) BCI paradigm, different subjects exhibit extremely different capacities at performing this task, and even different sessions from the same users can exhibit enough variation that classifiers trained in one session are illsuited to the next (Vidaurre ”
Introduction to machine learning for brain imaging (2011, background): “…of nonstationarity by (a) constructing invariant features (e.g. Blankertz et al., 2008), (b) tracking nonstationarity (e.g. Schlögl et al., 2010; Vidaurre and Blankertz, 2010; Vidaurre et al., 2011), (c) modeling nonstationarity and adapting CV schemes (Sugiyama et al., 2007), or by (d)…”