Simultaneous multiple-stimulus auditory brain–computer interface with semi-supervised learning and prior probability distribution tuning

Abstract

Objective. Auditory brain–computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However,existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli. Approach. For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR). Main results. The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ±11.46% and an ITR of 2.67 ± 1.09 bits/min, in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits/min, respectively. The ERP analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement. Significance. Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.

Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention

“…Although non-invasive BCI research and development often involve healthy subjects, studies have shown that brain-decoding functionality remains intact in individuals with impairments 39 , and can have positive impacts on those rehabilitating from strokes 40 . However, even with recent innovations, non-invasive BCIs still have online accuracy rates of approximately 70 to 80% 34 , 41 – 43 , leaving room for improvement before widespread clinical adoption.…”

Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention

“…As a result, non-invasive BCIs present a more feasible option for widespread application. Non-invasive BCIs have demonstrated ability to move a robotic arm 9,10 , control a wheelchair 11 , or type on a virtual keyboard 12,13 , but even recent innovations in non-invasive BCIs have online accuracy rates of approximately 70 to 80% 9, [14][15][16] , which leave room for improvement before seeking widespread clinical use.…”