Harmonic Component Analysis: A novel training-free and asynchronous SSVEP-classification method

Assistive technologies can provide people with locked-in syndrome independence and improve their quality of life. However, existing brain-computer interfaces (BCI) can
be unreliable and require excessive training. Therefore, we investigate the possibility of a training-free BCI that can provide asynchronous and online control of assistive robotic technologies. We propose the harmonic component analysis (HCA), a new training-free classifier for signals with known harmonic characteristics, such as steady state visually evoked potentials. To validate the HCA, it is compared to the well-known canonical correlation analysis (CCA), using an offline data set of 10 healthy participants who performed cue trials with 16 SSVEP-targets. The HCA achieved a better performance than a three-component CCA with an up to 74% lower computational cost. For asynchronous control, the HCA achieved a detection accuracy of 86% with an average activation time of 1.51s, against 78% after an average 1.57s for the CCA. For continuous activation, the HCA achieved a 68% true positive rate from 2s after cue-onset until 5s after, whereas the CCA achieved 61%. Both methods had a 1% false positive rate. Thus, the HCA is shown to be a well-suited SSVEP-classifier for systems that require asynchronous classification without the need for a
calibration or training-session.