July 20, 2006Journal of Neural Engineering

Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time–frequency tilings

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Population

9 subjects of the BCI Competition 2002

Comparison

Adaptive time-frequency analysis of EEG signals… vs Adaptive autoregressive model based…

Authors

NINuri Finodotrat InceUniversity of Minnesota SASami ArıcaCukurova University ATAhmed H. TewfikRutgers, The State University of New Jersey

Discussion

Member takes

Implication

May enhance motor imagery EEG classification accuracy; leaves open translation to clinical brain-computer interfaces.

Structured PICO

Population

9 subjects of the BCI Competition 2002

Intervention

Adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP)

Comparator

Adaptive autoregressive model based classification procedure

Outcome

Classification performance/accuracysurrogate

The proposed adaptive time-frequency analysis technique improves the classification accuracy of motor imagery EEG recordings compared to standard adaptive autoregressive models.

Cite This Study

Ince et al. (2006) studied this question.

synapsesocial.com/papers/6a8492c67b4d6f59a47c19aa https://doi.org/10.1088/1741-2560/3/3/006

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Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time–frequency tilings

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time–frequency tilings

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider