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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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Graphical interface of the reference P300-based BCI [13]. The four icons represent four different basic needs. The flashing arrow on the left side is an example of stimulation.
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fig1: Graphical interface of the reference P300-based BCI [13]. The four icons represent four different basic needs. The flashing arrow on the left side is an example of stimulation.

Mentions: Data utilized in the present study for offline analyses are those recorded during the online BCI sessions described in [13] from 21 ALS patients (aged 55.6 ± 14.3 years), in early and middle stages of the disease (32.2 ± 6.7 ALSFRS-R score [40], 47 ± 32 months from the disease onset), and 9 healthy controls (aged 54 ± 18.8 years). We refer the reader to [13] for details. Briefly, users were faced with a monitor with four images at its borders, representing four basic needs, for example, being hungry, and a circle in the center, as shown in Figure 1. The stimulation consisted of consecutive blocks of randomized flashings of four arrows (upward, rightward, downward, and leftward), each pointing to the direction of one image. By focusing on the flashing of the arrow pointing to the desired image (target stimulus) and ignoring the others (nontarget stimuli), a P300 component was elicited. EEG was recorded from four channels, that is, Fz, Cz, Pz, and Oz, and the electrooculogram (EOG) was recorded from two electrodes placed laterally and below the left eye. All electrodes were referenced to the left earlobe. Signals were amplified by a SynAmps (NeuroSoft, Inc.) amplifier, band-pass filtered between 0.15 and 30 Hz, digitized with a 16-bit resolution, and sampled at 200 Hz. After each flashing, a detection of a P300 activity from the measured EEG determined the movement of the circle by one step towards the direction of the flashed arrow and four consecutive steps in the desired direction were needed to reach the image. The time interval between two consecutive flashings, that is, the inter stimulus interval (ISI), was 2.5 s. Each BCI session started with the circle at the center of the screen and ended when the user reached the desired image or a time-out occurred (defined below for testing sessions only).


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)

Graphical interface of the reference P300-based BCI [13]. The four icons represent four different basic needs. The flashing arrow on the left side is an example of stimulation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109663&req=5

fig1: Graphical interface of the reference P300-based BCI [13]. The four icons represent four different basic needs. The flashing arrow on the left side is an example of stimulation.
Mentions: Data utilized in the present study for offline analyses are those recorded during the online BCI sessions described in [13] from 21 ALS patients (aged 55.6 ± 14.3 years), in early and middle stages of the disease (32.2 ± 6.7 ALSFRS-R score [40], 47 ± 32 months from the disease onset), and 9 healthy controls (aged 54 ± 18.8 years). We refer the reader to [13] for details. Briefly, users were faced with a monitor with four images at its borders, representing four basic needs, for example, being hungry, and a circle in the center, as shown in Figure 1. The stimulation consisted of consecutive blocks of randomized flashings of four arrows (upward, rightward, downward, and leftward), each pointing to the direction of one image. By focusing on the flashing of the arrow pointing to the desired image (target stimulus) and ignoring the others (nontarget stimuli), a P300 component was elicited. EEG was recorded from four channels, that is, Fz, Cz, Pz, and Oz, and the electrooculogram (EOG) was recorded from two electrodes placed laterally and below the left eye. All electrodes were referenced to the left earlobe. Signals were amplified by a SynAmps (NeuroSoft, Inc.) amplifier, band-pass filtered between 0.15 and 30 Hz, digitized with a 16-bit resolution, and sampled at 200 Hz. After each flashing, a detection of a P300 activity from the measured EEG determined the movement of the circle by one step towards the direction of the flashed arrow and four consecutive steps in the desired direction were needed to reach the image. The time interval between two consecutive flashings, that is, the inter stimulus interval (ISI), was 2.5 s. Each BCI session started with the circle at the center of the screen and ended when the user reached the desired image or a time-out occurred (defined below for testing sessions only).

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