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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The EmoReIQ (Emotion Recognition for Iraqi Autism Individuals) dataset is a specialized EEG dataset designed to capture emotional responses in individuals with Autism Spectrum Disorder (ASD) and Typically Developed (TD). It focuses on five core emotions: calm, happy, anger, fear, and sad. The dataset is gathered through an experimental setup using video stimuli to elicit these emotions and records corresponding EEG signals from participants.
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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.
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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.
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This dataset contains EEG error-related potential signals elicited by humans while observing an AI agent play an atari-based maze game.
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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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