Neuroscience

One of the grand challenges in neuroscience is to understand the developing brain ‘in action and in context’ in complex natural settings. To address this challenge, it is imperative to acquire brain data from freely-behaving children to assay the variability and individuality of neural patterns across gender and age.

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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.

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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

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This paper presents a dataset of brain Electroencephalogram (EEG) signals created when Malayalam vowels and consonants are spoken. The dataset was created by capturing EEG signals utilizing the OpenBCI Cyton device while a volunteer spoke Malayalam vowels and consonants. It includes recordings obtained from both sub-vocal and vocal. The creation of this dataset aims to support individuals who speak Malayalam and suffer from neurodegenerative diseases.

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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.

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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.

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This is the supplementary document for the review paper titled “Comprehensive and Data-Driven Literature Review of Supernumerary Robotic Limbs,” which presents a comprehensive and data-driven review that offers a quantitative analysis of Supernumerary Robotic Limbs (SRLs), covering application areas, structural designs, control strategies, embodiments, and their interconnections.

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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.

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Resilience is an important indicator of our defence mechanism against mental illness. Its assessment is conventionally done using psychological questionnaires and has also been recently investigated using neuroimaging modalities. These modalities provide objective and physiological-based assessment of resilience for prognosis and training purposes such as in the neurofeedback and behavioural therapies. This study investigates the use of electroencephalogram (EEG) to assess mental resilience in 2-Class, 3-Class and 4-Class models during resting and task conditions.

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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.

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