Many stroke survivors are unable to effectively control brain-computer interface (BCI) devices due to insufficient sensorimotor activity generated during motor imagery. Previous studies focused on upregulating motor cortex excitability and overlooked the important role that motor imagery plays on BCI control. Dorsolateral prefrontal cortex (DLPFC), an important region for motor imagery, may serve as an effective target for improving BCI performance.
A high-quality dataset is essential for validating the effectiveness and accuracy of the proposed compression method. To assess the feasibility of the research methodology, we collected neural signals from 96 channels recorded from two non-human primates using the Blackrock Microsystems system and the Blackrock Cerebus, with a sampling rate of 30 kHz.
Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.
