Brain
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 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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This dataset consists of MRI images of brain tumors, specifically curated for tasks such as brain tumor classification and detection. The dataset includes a variety of tumor types, including gliomas, meningiomas, and glioblastomas, enabling multi-class classification. Each MRI scan is labeled with the corresponding tumor type, providing a comprehensive resource for developing and evaluating machine learning models for medical image analysis. The data can be used to train deep learning algorithms for brain tumor detection, aiding in early diagnosis and treatment planning.
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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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This is a collection of scripts to perform essential preprocessing steps,
doing denoise and feature extraction of EEG (ERP) data using MATLAB and the EEGLAB toolbox.
The scripts are under current development with no guarantee of proper
functioning. The scripts are published in the hopes of helping people with interests in our experiment to reproduce the steps under their environment, and maybe extend to further improvement in the future. Currently, this project works with Muse S band (cortex TP9, TP10, AF7, AF8) device and is implemented with XDF raw Data.
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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 comprises a comprehensive collection of augmented MRI images of brain tumors, organized into two distinct folders: 'Yes' and 'No'. The 'Yes' folder contains 9,828 images of brain tumors, while the 'No' folder includes 9,546 images that do not exhibit brain tumors, resulting in a total of 19,374 images. All images are in PNG format, ensuring high-quality and consistent resolution suitable for various machine learning and medical imaging research applications.
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