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Classification of erroneous actions using EEG frequency features: implications for BCI performance.

Camila Dias, Diana M Costa, Teresa Sousa

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
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    This study introduces a new algorithm using frequency features to detect error-related brain signals in complex tasks, improving brain-computer interface performance. Frequency features show better accuracy than temporal features for identifying errors before response execution.

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    Area of Science:

    • Neuroscience
    • Brain-Computer Interfaces
    • Signal Processing

    Background:

    • Error-related neuronal signatures are detectable and can enhance brain-computer interface (BCI) performance.
    • Previous research primarily focused on controlled settings and temporal features like event-related potentials.
    • A gap exists in understanding error detection in complex tasks using advanced signal processing.

    Purpose of the Study:

    • To develop and evaluate a classification algorithm for detecting error-related neuronal signatures in a complex saccadic go/no-go task.
    • To investigate the efficacy of combining frequency and temporal features versus using each alone.
    • To test the hypothesis that frequency features offer superior discrimination and generalization in complex tasks.

    Main Methods:

    • A classification algorithm combining frequency features and a weighted Support Vector Machine (SVM) was proposed.
    • The algorithm was applied to data from a complex saccadic go/no-go task.
    • Performance was evaluated using balanced classification accuracy, comparing temporal, frequency, and combined feature sets.

    Main Results:

    • Combining temporal and frequency features achieved a balanced classification accuracy of 75%.
    • Using only frequency features yielded similar accuracy to the combined approach.
    • Relying solely on temporal features resulted in a lower balanced accuracy of 66%.

    Conclusions:

    • Frequency features are effective for automatically detecting subjects' performance based on error-related neuronal signatures in complex tasks.
    • Frequency features demonstrate superior or equivalent performance compared to temporal features, requiring potentially less pre-processing.
    • Error-related neural patterns exist even before response execution, as indicated by pre-response time feature analysis.