Harmonic Component Analysis: A novel training-free and asynchronous BCI 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 better performance than a three-component CCA with up to 74% lower computational cost. For asynchronous control, the HCA achieved a detection accuracy of 85% with an average activation time of 1.6s, against 77% after an average of 1.7s for the CCA. For continuous activation, the HCA achieved a true positive rate of 65% with a false positive rate of 0. 59% from 2 s after cue onset until 5 s after, while the CCA achieved a true positive rate of 59% with a false positive rate of 0. 27%. 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.