Neuroscience
Stress became a common factor of individuals in this competitive work environment, especially in academics. To address and assess this issue, this MUSEI-EEG dataset provides the Electroencephalogram (EEG) data of 20 undergraduate individuals in the 18-24 years age group (both male and female). Raag Darbari's music-based three-stage paradigm is designed for the subjects for cognitive stress assessment. Through this paradigm, physiological signal-based monitoring of stress level reduction can be observed in reference to stress and anxiety forms filled by the individual.
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data for different PUF designs that has been implemented on different FPGA for making a final comparision Table for new PUF disigns and some conventional ones. These data can be useful for any Hardware security implementation to make the decision regarding a PUF. These can be used when anyone need to extract Crptographic KEY.
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Transcranial Magnetic Stimulation (TMS) is a neuromodulation procedure used to treat psychiatric and neurological disorders. When electroencephalography (EEG), a neuroimaging technique, is applied in conjunction with TMS, the analysis of resting-state EEG activity can be used to quantify functional connectivity (FC) in the brain. These modulations can then be related to a subject’s resting motor threshold (RMT), a baseline parameter in TMS therapy that determines the treatment intensity (dose) of subjects undergoing TMS.
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Natural wood is generally perceived as expensive, while wood-based boards are cheap, but research has yet to definitively explain why these two materials receive such disparate evaluations. This study aimed to uncover the reasons for this phenomenon by proposing an emotion-driven approach to material research. We collected ten types of natural wood and artificial boards as experimental samples and tested 20 subjects' subjective evaluations of the samples and physiological indicators (electroencephalography and electrodermal activity).
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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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631 Views
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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2556 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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407 Views