Abstract
Recent experiments have shown the possibility of using the brain electrical activity to directly control the movement of robots or prosthetic devices in real time. Such neuroprostheses can be invasive or non-invasive, depending on how the brain signals are recorded. In principle, invasive approaches will provide a more natural and flexible control of neuroprostheses, but their use in humans is debatable given the inherent medical risks. Non-invasive approaches mainly use scalp electroencephalogram (EEG) signals and their main disadvantage is that these signals represent the noisy spatiotemporal overlapping of activity arising from very diverse brain regions, i.e., a single scalp electrode picks up and mixes the temporal activity of myriads of neurons at very different brain areas. In order to combine the benefits of both approaches, we propose to rely on the non-invasive estimation of local field potentials (LFP) in the whole human brain from the scalp measured EEG data using a recently developed inverse solution (ELECTRA) to the EEG inverse problem. The goal of a linear inverse procedure is to de-convolve or un-mix the scalp signals attributing to each brain area its own temporal activity. To illustrate the advantage of this approach we compare, using an identical set of spectral features, classification of rapid voluntary finger self-tapping with left and right hands based on scalp EEG and non-invasively estimated LFP on two subjects using a different number of electrodes.
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Notes
This is possible because the operation of the brain interface is asynchronous and, unlike synchronous approaches (Wolpaw and McFarland 1994; Birbaumer et al. 1999; Donchin et al. 2000; Roberts and Penny 2000; Pfurtscheller and Neuper 2001), does not require waiting for external cues that arrive at a fixed pace of 4–10 s.
This projection is based on the best-fitting sphere with center and radius selected to fit the scalp region used by the electrodes. This method requires the careful positioning of the electrodes based on anatomical landmarks, i.e., vertex electrode (Cz), middle line, frontal electrodes (Fp) etc. Note that, due to the inaccuracies of boundary detection algorithms, there is no rigid transformation able to “land” a set of electrodes on the scalp detected from the MRI. For this reason, most landing procedures need, at some stage, to project electrode positions on the detected scalp. This procedure has been widely used and tested in clinical studies using standard EEG configurations (e.g., 10-20 and 10-10 systems) where the subject’s MRI is not available as well as in the construction of realistic head models for presurgical evaluation of epileptic patients. Still, minor differences between electrode locations might be expected from one session to another. These variations can be considered as noise in the data and its influence can be alleviated with regularization strategies.
After a visual a posteriori artifact check of the trials, we found no evidence of muscular artifacts that could have contaminated one condition differently from the other.
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Acknowledgements
This work was supported by the Swiss National Science Foundation through the National Center of Competence in Research on “Interactive Multimodal Information Management (IM2)” and also by the Swiss National Science Foundation grant 3152A0-100745/1.
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Communicated by Irene Ruspantini and Niels Birbaumer
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Grave de Peralta Menendez, R., González Andino, S., Perez, L. et al. Non-invasive estimation of local field potentials for neuroprosthesis control. Cogn Process 6, 59–64 (2005). https://doi.org/10.1007/s10339-004-0043-x
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DOI: https://doi.org/10.1007/s10339-004-0043-x