EEG
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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2525 Views
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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3972 Views
The dataset consists of EEG recorded non-invasively from five participants while they are performing a handwriting imagery task, in which each participant was instructed to imagine the process of handwriting the 26 English letters. Major preprocessing steps have been conducted, including large-amplitude artifact removal using Independent Component Analysis and bandpass filtering between 0.1-45 Hz.
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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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615 Views
In this study, we collected EEG and EMG data from 16 subjects during the MI process and constructed a homemade MI-hBCI dataset. The participants included 10 males (mean age: 22.3±3.1 years) and 6 females (mean age: 22.1±2.4 years). All the subjects were right-handed, had normal vision, and had no motor impairment; all the participants signed a consent form and were informed of the experimental procedure and precautions before the experiment.
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107 Views
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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124 Views
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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2549 Views
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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398 Views
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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2787 Views
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.
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423 Views