Neuroscience

Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

This paper introduces a dataset capturing brain signals generated by the recognition of 100 Malayalam words, accompanied by their English translations. The dataset encompasses recordings acquired from both vocal and sub-vocal modalities for the Malayalam vocabulary. For the English equivalents, solely vocal signals were collected. This dataset is created to help Malayalam speaking patients with neuro-degenerative diseases.

Finger Scanning Experiments: Participants conducted experiments while seated and wearing an eye mask to eliminate visual information. They were instructed to horizontally scan the surface back-and-forth eight times with one of their fingers to assess surface roughness. The finger's motion was optically captured at a frame rate of 60 fps. Scanning speed was determined by measuring finger positions at each frame and dividing them by the frame length. Image analysis was performed using OpenCV, where the finger outline was extracted from the video.

This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of four di erent motor imagery tasks, namely the imag ination of movement of the left hand (class 1), right hand (class 2), both feet (class 3), and tongue (class 4). Two sessions on di erent days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session.