EEG

We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.

This dataset contains EEG error-related potential signals elicited by humans while observing an AI agent play an atari-based maze game.

To address the challenges faced by patients with neurodegenerative disorders, Brain-Computer Interface (BCI) solutions are being developed. However, many current datasets lack inclusion of languages spoken by patients, such as Telugu, which is spoken by over 90 million people in India. To bridge this gap, we have created a dataset comprising Electroencephalograph (EEG) signal samples of commonly used Telugu words. Using the Open-BCI Cyton device, EEG samples were captured from volunteers as they pronounced these words.

This dataset consists of electroencephalography (EEG) data from 6 participants aged between 23 and 28 years, with a mean age of 25 years. The dataset is the motor imagery EEG signals of six different rehabilitation training movements in the upper limbs. We recruited six participants aged between 23 and 28 years, with a mean age of 25 years. Three of the participants are male. Subjects sat in a comfortable chair, facing the computer monitor that displayed the trial-based paradigm and their right arm was naturally placed on the table to avoid muscle fatigue.