Researchers from Skoltech, INRIA and the RIKEN Advanced Intelligence Project have considered several state-of-the-art machine learning algorithms for the challenging tasks of determining the mental workload and affective states of a human brain.
Researchers from Skoltech, INRIA and the RIKEN Advanced Intelligence Project have considered several state-of-the-art machine learning algorithms for the challenging tasks of determining the mental workload and affective states of a human brain. Their software can help design smarter brain-computer interfaces for applications in medicine and beyond. The paper was published in the IEEE Systems, Man, and Cybernetics Magazine.
A brain-computer interface, or BCI, is a link between a human brain and a machine that can allow users to control various devices, such as robot arms or a wheelchair, by brain activity only (these are called active BCIs) or can monitor the mental state or emotions of a user and categorize them (these are passive BCIs). Brain signals in a BCI are usually measured by electroencephalography, a typically noninvasive method of recording electrical activity of the brain.
But there is quite a long way from raw continuous EEG signals to digitally processed signals or patterns that would have the ability to correctly identify a user’s mental workload or affective states, something that passive BCIs need to be functional. Existing experiments have shown that the accuracy of these measurements, even for simple tasks of, say, discriminating low from high workload, is insufficient for reliable practical applications.
Read more at Skolkovo Institute of Science and Technology (SKULTECH)
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