Key result
Applying Blind Source Separation at the beginning of the signal processing pipeline significantly improved Information Transfer Rate compared to applying it at the end (p=0.019).
Why the study?
Proper preprocessing steps are required to obtain maximally accurate stimuli recognition for SSVEP-based brain-computer interfaces.
p-value: p=0.019
For SSVEP-based brain-computer interfaces, applying blind source separation at the beginning of the preprocessing pipeline significantly improves decoding accuracy.
Brain-computer interface (BCI) is a device which allows paralyzed people to navigate a robot, prosthesis or wheelchair using only their own brains reactions. By creating a direct communication pathway between the human brain and a machine, without muscles contractions or activity from within the peripheral nervous system, BCI makes mapping persons intentions onto directive signals possible. One of the most commonly utilized phenomena in BCI is steady-state visually evoked potentials (SSVEP). If subject focuses attention on the flashing stimulus (with specified frequency) presented on the computer screen, a signal of the same frequency will appear in his or hers visual cortex and from there it can be measured. When there is more than one stimulus on the screen (each flashing with a different frequency) then based on the outcomes of the signal analysis we can predict at which of these objects (e.g., rectangles) subject was/is looking at that particular moment. Proper preprocessing steps have taken place in order to obtain maximally accurate stimuli recognition (as the specific frequency). In the current article, we compared various preprocessing and processing methods for BCI purposes. Combinations of spatial and temporal filtration methods and the proceeding blind source separation (BSS) were evaluated in terms of the resulting decoding accuracy. Canonical-correlation analysis (CCA) to signals classification was used.
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Jukiewicz et al. (2018) studied Healthy subjects (Brain-Computer Interface) (n=4). Blind Signal Separation (BSS) and filtering algorithms vs. Alternative processing pipelines or no processing was evaluated on Information Transfer Rate (ITR) (p=0.019). Applying Blind Source Separation at the beginning of the signal processing pipeline significantly improved Information Transfer Rate compared to applying it at the end (p=0.019).
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