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Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface.

Goljahani A, D'Avanzo C, Silvoni S, Tonin P, Piccione F, Sparacino G - Comput Math Methods Med (2014)

Bottom Line: A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives.Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities.Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy.

ABSTRACT
A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

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Preprocessing results for a representative ALS patient. Raw target and nontarget epochs collected from P8 in T4 are shown in (a) and (b), respectively, together with their average, shown as solid blue lines. In (c) and (d) two representative raw target and nontarget epochs (blue curves) are superimposed to their denoised versions obtained by the Bayesian preprocessing (red curves). In (e) and (f) signals obtained by preprocessing target and nontarget epochs in (a) and (b), respectively, together with their averages (red curves), are shown.
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fig3: Preprocessing results for a representative ALS patient. Raw target and nontarget epochs collected from P8 in T4 are shown in (a) and (b), respectively, together with their average, shown as solid blue lines. In (c) and (d) two representative raw target and nontarget epochs (blue curves) are superimposed to their denoised versions obtained by the Bayesian preprocessing (red curves). In (e) and (f) signals obtained by preprocessing target and nontarget epochs in (a) and (b), respectively, together with their averages (red curves), are shown.

Mentions: Before investigating the performance of the BCI system, it is useful to show an example of application of the single-trial estimation technique to our data. Figure 3 reports, in panels (a) and (b), respectively, single-trial target and nontarget raw epochs recorded from Pz in patient P8 during the testing day T4. The blue curves drawn in the same panels are the averages of target and nontarget raw epochs, respectively. Panels (c) and (d) display results of the new preprocessing step (red curves) for one representative target and one representative nontarget raw epoch (blue curves). Finally, the black curves in panels (e) and (f) are the preprocessed versions of all curves in panels (a) and (b), respectively, and the red curves are their averages. As visible from the blue curves in panels (a) and (b), target raw epochs are characterized by an average positive deflection that is not present in nontarget epochs. The deflection, which takes place at around 500 ms, is the P300 component of the ERP. The red curve in panel (c) shows that, at the single-trial level, the considered preprocessing smoothes away spurious oscillations and produces a signal in which the P300-related activity is more evident. As far as nontarget epochs are concerned, panel (d) confirms that, as expected, the proposed preprocessing yields a signal that is quite flat, reflecting the absence of a P300-related activity. Finally, panels (e) and (f) show how the extracted activity varies from epoch to epoch, with average activities (red curves) similar to the ones of raw epochs.


Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface.

Goljahani A, D'Avanzo C, Silvoni S, Tonin P, Piccione F, Sparacino G - Comput Math Methods Med (2014)

Preprocessing results for a representative ALS patient. Raw target and nontarget epochs collected from P8 in T4 are shown in (a) and (b), respectively, together with their average, shown as solid blue lines. In (c) and (d) two representative raw target and nontarget epochs (blue curves) are superimposed to their denoised versions obtained by the Bayesian preprocessing (red curves). In (e) and (f) signals obtained by preprocessing target and nontarget epochs in (a) and (b), respectively, together with their averages (red curves), are shown.
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Related In: Results  -  Collection

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fig3: Preprocessing results for a representative ALS patient. Raw target and nontarget epochs collected from P8 in T4 are shown in (a) and (b), respectively, together with their average, shown as solid blue lines. In (c) and (d) two representative raw target and nontarget epochs (blue curves) are superimposed to their denoised versions obtained by the Bayesian preprocessing (red curves). In (e) and (f) signals obtained by preprocessing target and nontarget epochs in (a) and (b), respectively, together with their averages (red curves), are shown.
Mentions: Before investigating the performance of the BCI system, it is useful to show an example of application of the single-trial estimation technique to our data. Figure 3 reports, in panels (a) and (b), respectively, single-trial target and nontarget raw epochs recorded from Pz in patient P8 during the testing day T4. The blue curves drawn in the same panels are the averages of target and nontarget raw epochs, respectively. Panels (c) and (d) display results of the new preprocessing step (red curves) for one representative target and one representative nontarget raw epoch (blue curves). Finally, the black curves in panels (e) and (f) are the preprocessed versions of all curves in panels (a) and (b), respectively, and the red curves are their averages. As visible from the blue curves in panels (a) and (b), target raw epochs are characterized by an average positive deflection that is not present in nontarget epochs. The deflection, which takes place at around 500 ms, is the P300 component of the ERP. The red curve in panel (c) shows that, at the single-trial level, the considered preprocessing smoothes away spurious oscillations and produces a signal in which the P300-related activity is more evident. As far as nontarget epochs are concerned, panel (d) confirms that, as expected, the proposed preprocessing yields a signal that is quite flat, reflecting the absence of a P300-related activity. Finally, panels (e) and (f) show how the extracted activity varies from epoch to epoch, with average activities (red curves) similar to the ones of raw epochs.

Bottom Line: A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives.Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities.Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy.

ABSTRACT
A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

Show MeSH
Related in: MedlinePlus