Electroencephalogram-based control of an electric wheelchair

By Kazuo Tanaka, K. Matsunaga, Hua O. Wang. IEEE Transactions on robotics, 2005

383 citations recorded by the source corpus.

This paper presents a study on electroencephalogram (EEG)-based control of an electric wheelchair. The objective is to control the direction of an electric wheelchair using only EEG signals. In other words, this is an attempt to use brain signals to control mechanical devices such as wheelchairs. To achieve this goal, we have developed a recursive training algorithm to generate recognition patterns from EEG signals. Our experimental results demonstrate the utility of the proposed recursive training algorithm and the viability of accomplishing direction control of an electric wheelchair by only EEG signals.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/f4939772c69893d0d7840ac40ace1b4bedb3ff2e

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Human-Computer Interaction

Citations in the corpus

Brain Computer Interfaces, a Review (2012, methodology): “A typical application of a BCI based on visual P300 evoked potentials comprises a matrix of letters, numbers, or other symbols or commands [123,127,128].”
Brain–computer interface systems: progress and prospects (2007, background): “Current versions of mobile robots are not yet useful to the BCI user population but may be stepping stones to more practical output devices, such as BCI-controlled wheelchairs [77,159] .”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, methodology): “One typical example is the work of Tanaka et al. [21], who ﬁrst developed a brain-controlled robotic wheelchair whose left or right turning movements are directly controlled by corresponding motion commands translated from user brain signals while imagining left or right limb movements, and tested…”