Brain Patterns Generated while Using a Tongue Control Interface: A Preliminary Study with Two Individuals with ALS.

Rasmus Leck Kæseler,Mads Jochumsen, Ana S. Santos Cardoso, Lotte N. S. Andreasen Struijk

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(2023)

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摘要
Individuals suffering from a progressive neurodegenerative disease, such as amyotrophic lateral sclerosis (ALS), will lose muscle function over time and become completely paralysed. For some time, people with ALS may retain functional tongue movement, despite losing mobility below the neck. These individuals can benefit from using an inductive tongue control interface (ITCI) to control computers or assistive robotic devices to gain independence in their daily lives. Eventually, when the individual can no longer use their tongue, they can rely on a brain computer interface (BCI). However, these require a lot of data to calibrate and function properly. Recording this data while the individual can still use the ITCI can potentially speed up the training process, allowing for an easier transition between interface technologies. This study investigates whether it is possible to create a background data collector for a BCI based on attempted tongue movement by analyzing brain patterns of two individuals with ALS while using an ITCI. The participants used an inductive ITCI in simple cued movement trials while electroencephalogram (EEG) was collected from the motor cortex. The EEG signal indicated that movement-related cortical potentials (MRCP) were generated after the cued movements. After synchronising the signal to the activations recorded on the ITCI, the MRCP became even more apparent. Therefore, it is concluded that it is possible to record MRCPs from individuals with ALS performing tongue movements, that the ITCI can assist in better extracting synchronized MRCP epochs, and that a background data collector for a tongue movement intention-based BCI is very feasible.
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关键词
Amyotrophic Lateral Sclerosis,Tongue Control,Motor Cortex,Assistive Technology,Robotic Assistance,Tongue Movement,Readiness Potential,Interface Technology,Caregivers,Random Sequence,Unit Of Activity,Global Minimum,Mastoid,Negative Peak,Robotic Arm,Spatial Filter,Movement Onset,Calibration Time,Synchronization Method,Brain-computer Interface Technology
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