Abstract
Future neuroprosthetic devices, in particular upper limb, will require decoding and executing not only the user's intended movement type, but also when the user intends to execute the movement. This work investigates the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated which could contribute to the design of practical upper limb neuroprosthetics. In particular, we show the detection of self-paced reaching movement intention in single trials using the readiness potential, an electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency range (0.1–1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects, yield high detection rates prior to the movement onset and low detection rates during the non-movement intention period. With the proposed approach, movement intention was detected around 500 ms before actual onset, which clearly matches previous literature on readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent with those achieved in healthy subjects, with single-trial performance of up to 92% for the paretic arm. These results suggest that, apart from contributing to our understanding of voluntary motor control for designing more advanced neuroprostheses, our work could also have a direct impact on advancing robot-assisted neurorehabilitation.
Highlights
Human movements are usually volitional, where we spontaneously decide when to initiate it and commit to a particular course of action to accomplish a daily task (Haggard, 2008)
The reason why, after the promising results achieved in the first experiment, we have run a second experiment with a small stroke cohort is to make a preliminary study on the feasibility to detect movement intention in single trials as a potential tool for rehabilitation
We show the detection of self-paced reaching movement intention in single trials from the analysis of the readiness potential, an EEG slow potentials that we compute in a narrow frequency range between 0.1 and 1 Hz
Summary
Human movements are usually volitional, where we spontaneously decide when to initiate it and commit to a particular course of action to accomplish a daily task (Haggard, 2008). This is the reason why uncovering the neural correlates of voluntary movement is important for implementing practical Brain Computer Interface (BCI) technology that people can use over long periods of time in a natural way. This definition is not to be confused with the work of Congedo et al (2006), Gonzalez et al (2006), and Bai et al (2007) where movement intention was defined as the problem of classifying the intention to move the left hand or right hand
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