A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

Figures

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  FIGURE 1. Region division. Channels belonging to a region are circled with the dotted line. The left subfigure shows the division for DS1 and DS2, while the right subfigure shows the division for DS3.

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  FIGURE 2. Parameter relationship graphical model in a single iteration. Parameters of the same color can be iterated simultaneously.

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  FIGURE 3. Diagram of the data processing framework, including pre-processing, channel selection, and classification. Using the block sparsity property of RSBSBL, we do pruning on the eligible channels by fitting the training data and labels.

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  FIGURE 4. The average recognition accuracy of the four methods on DS1, DS2, and DS3 when M channels are selected, where M = [4, 8, 12, 16]. The error bars are the standard deviations for DS2 and DS3.

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  FIGURE 5. The scalp distribution of the four methods on DS1, DS2, and DS3 when M channels are selected. The contribution value of each channel is equal to the sum of the selected numbers among all participants in the dataset. The color changes from red to blue, indicating that the channel is selected less often.

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  FIGURE 6. The effect of shear threshold τ in RSBSBL on the number of selected channels and accuracy. The x-axis indicates the number of selected channels, the y-axis indicates the value of τ, and the z-axis indicates the participant ID. The color of the sphere represents the normalized recognition accuracy for each participant with different thresholds.

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  FIGURE 7. Changes in the run-time (s) of matrix inversion when the size of the matrix increases. In the left part, the horizontal axis represents the size of the square array. The vertical axis is the value after taking the logarithm of the time. The bar chart represents the average time cost of the proposed method on the three datasets.