NeuroSense: A Novel EEG Dataset Utilizing Low-Cost, Sparse Electrode Devices for Emotion Exploration

README Link to the Publication Read the Paper Details related to access to the data Data user agreement The terms and conditions for using this dataset are specified in the [LICENCE](LICENCE) file included in this repository. Please review these terms carefully before accessing or using the data. Contact person For additional information about the dataset, please contact:- Name: Angela Lombardi- Affiliation: Department of Electrical and Information Engineering, Politecnico di Bari- Email: angela.lombardi@poliba.it Practical information to access the data The dataset can be accessed through our dedicated web platform. To request access: 1. Visit the main dataset page at: https://sisinflab.poliba.it/neurosense-dataset-request/2. Follow the instructions on the website to submit your access request3. Upon approval, you will receive further instructions for downloading the data Please ensure you have read and agreed to the terms in the data user agreement before requesting access. Overview EEG Emotion Recognition - Muse Headset2023-2024 The experiment consists in 40 sessions per user. During each session, users are asked to watch amusic video with the aim to understand their emotions. Recordings are performed with a Muse EEG headset at a 256 Hz sampling rate. Channels are recorded as follows:- Channel 0: AF7- Channel 1: TP9- Channel 2: TP10- Channel 3: AF8 The chosen songs have various Last.fm tags in order to create different feelings. The title of every trackcan be found in the "TaskName" field of sub-ID***_ses-S***_task-Default_run-001_eeg.json, while the author,the Last.fm tag and additional information in "TaskDescription". Methods Subjects The subject pool is made of 30 college students, aged between 18 and 35. 16 of them are males, 14 females. Apparatus The experiment was performed using the same procedures as those to create[Deap Dataset](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/), which is a dataset to recognize emotions via a BrainComputer Interface (BCI). Task organization Firstly, music videos were selected. Once 40 songs were picked, the protocol was chosen and the self-assessmentquestionnaire was created. Task details In order to evaluate the stimulus, Russell's VAD (Valence-Arousal-Dominance) scale was used. In this scale, valenza-arousal space can be divided in four quadrants:- Low Arousal/Low Valence (LALV);- Low Arousal/High Valence (LAHV);- High Arousal/Low Valence (HALV);- High Arousal/High Valence (HAHV). Experimental location