Stimuli and Feature Extraction Algorithms for Brain-Computer Interfaces: a systematic comparison

A brain-computer interface (BCI) is a system that allows communication between the central nervous system and an external device. The BCIs developed by various research groups differ in their main features and the comparison across studies is therefore challenging. Here, in the same group of 19 healthy participants, we investigate three different tasks (SSVEP, P300 and hybrid) that allowed four choices to the user without previous neurofeedback training. We used the same 64-channel EEG equipment to acquire data while participants performed each of the tasks. We systematically compared the participants’ offline performance on the following parameters: a) accuracy, b) BCI Utility (in bits/min) and, c) inefficiency/illiteracy. Additionally, we evaluated the accuracy as a function of the number of electrodes. In our study, the SSVEP task outperformed the other tasks in bit rate, reaching an average and maximum BCI Utility of 63.4 bits/min and 91.3 bits/min, respectively. All participants achieved an accuracy level above 70% on both SSVEP and P300 tasks. Further, the average accuracy of all tasks was highest if a reduced subset with four to 12 electrodes was used. These results are relevant for the development of online BCIs intended for real-life applications