A new Swiss study has revealed that machines are better able to interpret human thoughts when users undergo specific training, marking a significant advancement in brain-machine interface (BMI) technology.
Conducted by researchers at the University of Geneva, the study focused on how training impacts a machine’s ability to interpret brain signals. Fifteen volunteers participated, using electrodes to record brain activity as they imagined saying the syllables “fo” and “gi.” Over five days, the participants received real-time feedback to help them improve communication with the machine.
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The trained group showed substantial improvements in machine accuracy compared to a control group that received inconsistent feedback. This breakthrough could lead to new communication methods for people with speech impairments caused by strokes or neurological conditions.
Researchers emphasize that consistent training is essential for improving BMI systems’ reliability, offering hope for more effective thought-based communication technologies.
