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Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG).

Brunner P, Ritaccio AL, Emrich JF, Bischof H, Schalk G - Front Neurosci (2011)

Bottom Line: The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min).Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system.Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.

View Article: PubMed Central - PubMed

Affiliation: New York State Department of Health, Brain-Computer Interface Research and Development Program, Wadsworth Center Albany, NY, USA.

ABSTRACT
A brain-computer interface (BCI) can provide a non-muscular communication channel to severely disabled people. One particular realization of a BCI is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroencephalography (EEG) and were limited in their communication performance to only a few characters per minute. In our study, we investigated the feasibility of using electrocorticographic (ECoG) signals for online operation of the matrix speller, and determined associated spelling rates. We used the matrix speller that is implemented in the BCI2000 system. This speller used ECoG signals that were recorded from frontal, parietal, and occipital areas in one subject. This subject spelled a total of 444 characters in online experiments. The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min). Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system. Our results also point to potential reasons for the apparent advantages in spelling performance of ECoG compared to EEG. Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.

No MeSH data available.


Qualitative results. The figure at the top shows the locations of the 96 subdural electrodes (blue dots), as well as the color-coded single-flash classification accuracy at each individual electrode.The traces at the bottom show the correlation between ECoG amplitude and the type of the stimulus (target/non-target) for cortical locations A–G.
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Figure 5: Qualitative results. The figure at the top shows the locations of the 96 subdural electrodes (blue dots), as well as the color-coded single-flash classification accuracy at each individual electrode.The traces at the bottom show the correlation between ECoG amplitude and the type of the stimulus (target/non-target) for cortical locations A–G.

Mentions: The results presented in the previous section demonstrated that the BCI system successfully predicted the intended character online with an accuracy of 81% using only one flash of each row/column. We were interested in the physiological basis for this successful demonstration, i.e., in the cortical locations and ERP components that held significant information. To do this, we trained the classifier separately on each location using the calibration data with a flash duration of 3/64 s, and evaluated performance on the online data with the same flash duration and 1–3 flash sequences. Figure 5 shows the locations of all 96 subdural electrodes (blue dots) and the corresponding color-coded classification accuracies. Accuracy ranged from chance level (1/(6 × 6) = 2.8%) to 50% for the best electrode location.


Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG).

Brunner P, Ritaccio AL, Emrich JF, Bischof H, Schalk G - Front Neurosci (2011)

Qualitative results. The figure at the top shows the locations of the 96 subdural electrodes (blue dots), as well as the color-coded single-flash classification accuracy at each individual electrode.The traces at the bottom show the correlation between ECoG amplitude and the type of the stimulus (target/non-target) for cortical locations A–G.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037528&req=5

Figure 5: Qualitative results. The figure at the top shows the locations of the 96 subdural electrodes (blue dots), as well as the color-coded single-flash classification accuracy at each individual electrode.The traces at the bottom show the correlation between ECoG amplitude and the type of the stimulus (target/non-target) for cortical locations A–G.
Mentions: The results presented in the previous section demonstrated that the BCI system successfully predicted the intended character online with an accuracy of 81% using only one flash of each row/column. We were interested in the physiological basis for this successful demonstration, i.e., in the cortical locations and ERP components that held significant information. To do this, we trained the classifier separately on each location using the calibration data with a flash duration of 3/64 s, and evaluated performance on the online data with the same flash duration and 1–3 flash sequences. Figure 5 shows the locations of all 96 subdural electrodes (blue dots) and the corresponding color-coded classification accuracies. Accuracy ranged from chance level (1/(6 × 6) = 2.8%) to 50% for the best electrode location.

Bottom Line: The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min).Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system.Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.

View Article: PubMed Central - PubMed

Affiliation: New York State Department of Health, Brain-Computer Interface Research and Development Program, Wadsworth Center Albany, NY, USA.

ABSTRACT
A brain-computer interface (BCI) can provide a non-muscular communication channel to severely disabled people. One particular realization of a BCI is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroencephalography (EEG) and were limited in their communication performance to only a few characters per minute. In our study, we investigated the feasibility of using electrocorticographic (ECoG) signals for online operation of the matrix speller, and determined associated spelling rates. We used the matrix speller that is implemented in the BCI2000 system. This speller used ECoG signals that were recorded from frontal, parietal, and occipital areas in one subject. This subject spelled a total of 444 characters in online experiments. The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min). Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system. Our results also point to potential reasons for the apparent advantages in spelling performance of ECoG compared to EEG. Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.

No MeSH data available.