GOMEZ-RIVERA, Y. A. et al. BCI-based real-time processing for implementing deep learning frameworks using motor imagery paradigms. J. appl. res. technol []. 2024, 22, 5, pp.646-653.   11--2025. ISSN 2448-6736.  https://doi.org/10.22201/icat.24486736e.2024.22.5.2392.

As a cognitive process, motor imagery (MI) includes simulating motor actions mentally in the absence of physical movement. It has a variety of uses, including assistive technologies, medical diagnosis and rehabilitation. Motor imagery paradigms are utilized in conjunction with brain computer interfaces (BCI), which use electroencephalographic recordings (EEG) because of their high temporal resolution, cheap cost, portability, and non-invasiveness. Brain computer interfaces apply motor imagery paradigms by directly connecting the human brain to a computer. However, because scalp readings are non-stationary and non-linear, real-time processing of electroencephalographic recordings signals is challenging. Furthermore, in order to minimize the impact of outside noise and artifacts, clinical motor imagery methods must be implemented under carefully monitored laboratory conditions. A deep learning model-based approach is shown for analyzing electroencephalographic recordings data and giving real-time feedback to a brain computer interface. Generally, the system’s design is portable and low-cost, allowing the motor imagery paradigm to perform under poorly regulated sampling conditions.

: On-line feedback; EEG; BCI; MI; closed loop.

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