Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project

By José del R. Millán, J. Mouriño. IEEE transactions on neural systems and rehabilitation engineering, 2003

319 citations recorded by the source corpus.

In this communication, we give an overview of our work on an asynchronous brain-computer interface (where the subject makes self-paced decisions on when to switch from one mental task to the next) that responds every 0.5 s. A local neural classifier tries to recognize three different mental tasks; it may also respond "unknown" for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with 15 subjects. We also briefly describe two brain-actuated applications we have developed: a virtual keyboard and a mobile robot (emulating a motorized wheelchair).

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/aa357129c9fb0a69379023e32a4eab255673e931

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Citations in the corpus

Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic (2007, background): “Asynchronous BCIs are much more complicate than synchronous ones, nevertheless, the community is more and more addressing these problems [17–21].”
Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing (2006, unknown): “[2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13].”
Control of an Electrical Prosthesis With an SSVEP-Based BCI (2008, background): “Birch et al. [7] presented the low-frequency asynchronous switch design (LF-ASD) with hit rates above 70% and a false positive rate below 3%. An asynchronous system for selection of letters and the control of a mobile robot was reported by Millan et al. [ 8 ].”
EEG feature extraction based on wavelet packet decomposition for brain computer interface (2008, background): “Keywords: Brain computer interface (BCI); Wavelet packet decomposition (WPD); Feature extraction; Energy of sub-band”
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, background): “In del Millán and Mouriño (2003) it was reported that with this system a motorized wheelchair and a virtual keyboard could be controlled.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “In BCIs, the MLP has been applied to classify two, three, and ﬁve tasks and to design synchronous (Haselsteiner and Pfurtscheller, 2000) as well as asynchronous (Millan and Mourino, 2003) BCIs.”
Brain Computer Interfaces, a Review (2012, methodology): “In the field of BCIs, MLP have been applied to classify two [249], three [200], and five [250] different tasks, and to design synchronous [251] and asynchronous [215] BCIs.”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, unknown): “Studies have shown that BCIs can achieve independent multidimensional control, which can be utilized in many rehabilitation applications, such as modulating environment [75], [93], communication [25]–[27], [31], orthosis control [77], and navigation in 2-D [78] or 3-D space [19], etc.”