Brain

We developed IIST BCI Dataset-9, a novel EEG-based Brain-Computer Interface (BCI)
dataset to improve wheelchair control systems using Malayalam dialect variations. BCI
systems help people with motor disabilities by allowing them to control devices using brain
signals. The limited number of BCI datasets in Indian languages makes it harder for native
speakers to use these systems. To address this, we created a dataset with 15 Malayalam
words related to basic wheelchair commands like Forward, Backward, Go, Stop, Reverse,

Abstract

PassengerEEG is a brain-signal dataset designed to study how human passengers perceive and cognitively respond to potential traffic hazards in highly automated vehicles (AVs). As AVs increasingly replace human drivers, understanding passenger cognition becomes essential for improving vehicle safety and adaptive decision-making.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder affecting children and adolescents, characterized by inattention, hyperactivity, and impulsivity. Current diagnostic methods primarily rely on subjective clinical evaluations, which are prone to bias. Advances in neurophysiological assessment, particularly through electroencephalography (EEG), eye tracking, and electrodermal activity (EDA), offer promising avenues for objective diagnosis and monitoring of ADHD.

The Fuyong dataset records 134 stroke patients who received treatment in the Shenzhen Fuyong People's Hospital between March 1, 2022, and September 31, 2024. Besides, medical records of 435 stroke patients treated in the Affiliated Taizhou People's Hospital of Nanjing Medical University between January 1, 2020, and December 31, 2023, are included in the Taizhou dataset. These two datasets use the pre- and post-thrombolysis of the NIHSS scores as a metric for evaluating the immediate efficacy of the thrombolytic intervention.

EEG signals in the NMT-5k dataset were captured using a 21-channel setup following the 10/20 electrode placement system, maintaining a sampling rate of 200 Hz. Data collection was conducted using the KT88-2400 system from Contec Medical Systems.

This dataset provides measurements of cerebral blood flow using Radio Frequency (RF) sensors operating in the Ultra-Wideband (UWB) frequency range, enabling non-invasive monitoring of cerebral hemodynamics. It includes blood flow feature data from two arterial networks, Arterial Network A and Arterial Network B. Statistical features were manually extracted from the RF sensor data, while autonomous feature extraction was performed using a Stacked Autoencoder (SAE) with architectures such as 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128.

Abstract

This is a videoconference between a witness about murders who is a victim of many crimes and a law firm. This witness is called Colin Paul Gloster. This law firm is called Pais do Amaral Advogados.

\begin{quotation}``A significant part of the background of eh this process is that Hospital

Sobral Cid tortured me during 2013 eh but eh this process is actually

about consequences eh thru through a later process [. . .] To obscure eh

this torture of 2013, eh a new show trial was intitiated. Its process was

Transfer RNAs (tRNAs) are a family of non-coding ribonucleic acids (RNAs) that are involved in protein translation. Cleavage of tRNA through stress-induced ribonucleases results in the production of short tRNA-derived small RNAs (tsRNAs), such as tRNA-derived fragments (tRFs). Recent studies have identified three tRFs - 5'AlaTGC, 5'GluCTC, and 5'GlyGCC - to be released from neurons and significantly elevated in epilepsy patients. Extracellular 5'tRFs are taken up by neurons and act in paracrine to inhibit protein translation.

With the continuous advancement of technology, small and portable physiological sensors that can be worn on the body are quietly integrating into our daily lives, and are expected to greatly enhance our quality of life. In order to further enrich and expand the emotional physiological signals captured by portable wearable devices, we utilized the 14-channel portable EEG acquisition device Emotiv EPOC X, and with emotional video clips as the stimulus source, we collected two sets of emotional EEG signals from two groups of 10 participants each, named EmoX1 and EmoX2.