Biophysiological Signals

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 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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Developing mind-controlled prosthetics that seamlessly integrate with the human nervous system is a significant challenge in the field of bioengineering. This project investigates the use of labelled brainwave patterns to control a bionic arm equipped with a sense of touch. The core objective is to establish a communication channel between the brain and the artificial limb, enabling intuitive and natural control while incorporating sensory feedback.
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The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).

All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.

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The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).

The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).

Each file consists of a signals from one sigle ride.

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The AnxiECG-PPG Database contains synchronized electrocardiogram (ECG) and mobile-acquired photoplethysmography (PPG) recordings from 47 healthy participants. Moreover, the acquisition protocol assesses three distinct states: a 5-minute Baseline, a 1-minute Physical Activated State, and a Psychological Activated state provoked through emotion-induced videos (negative, positive, and neutral emotion valence).

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The dataset Arabic Digit Sequential Electromyography (ADSE) is acquired for eight-lead sEMG data targeting sequential signals.

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