Improved Target Recognition Response Using Collaborative Brain-Computer Interfaces

The advantage of using collaborative brain-computer interfaces in improving human response in visual target recognition tests was investigated. We used a public EEG dataset created from recordings made using a 32-channel EEG system by Delorme et al. (2004) to compare the classification accuracy using one, two, and three EEG signal sets from different subjects. Fourteen participants performed a go/ no-go categorization task on images that were presented very briefly, with the target images of natural photos of animals and distractor images of photos that did not contain animals. First, we compared the EEG responses evoked by the target and distractor images, and it was determined that the P300 (i. e., a positive deflection in voltage with a latency of 300 ms) response evoked by the target images was significantly higher than that evoked by the distractor images. Second, we calculated and compared the classification accuracy using one, two, and three EEG signal sets. We used a linear support vector machine with 5fold cross validation. Compared to the results obtained from single brain prediction (79.4%), the overall accuracy of two-and three-brains prediction was higher (89.3% and 88.7%, respectively). Furthermore, the time required to achieve 90% accuracy was significantly less when using EEGs from two and three brains (100 ms) than when using one brain (230 ms). These results provide evidence to support the hypothesis that one can achieve higher levels of perceptual and cognitive performance by leveraging the power of multiple brains through collaborative brain-computer interfaces.