MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks
Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpfu...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923006406 |
| _version_ | 1827794269315792896 |
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| author | Víctor Asanza Leandro L. Lorente-Leyva Diego H. Peluffo-Ordóñez Daniel Montoya Kleber Gonzalez |
| author_facet | Víctor Asanza Leandro L. Lorente-Leyva Diego H. Peluffo-Ordóñez Daniel Montoya Kleber Gonzalez |
| author_sort | Víctor Asanza |
| collection | DOAJ |
| description | Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition. |
| first_indexed | 2024-03-11T18:31:17Z |
| format | Article |
| id | doaj.art-c437648568a44467b38a5af12766e638 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-11T18:31:17Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-c437648568a44467b38a5af12766e6382023-10-13T11:04:59ZengElsevierData in Brief2352-34092023-10-0150109540MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasksVíctor Asanza0Leandro L. Lorente-Leyva1Diego H. Peluffo-Ordóñez2Daniel Montoya3Kleber Gonzalez4SDAS Research Group (https://sdas-group.com/), Ben Guerir 43150, MoroccoSDAS Research Group (https://sdas-group.com/), Ben Guerir 43150, Morocco; Faculty of Law, Administrative and Social Sciences, Universidad UTE, Quito 170147, Ecuador; Corresponding author at: SDAS Research Group (https://sdas-group.com/), Ben Guerir 43150, Morocco.SDAS Research Group (https://sdas-group.com/), Ben Guerir 43150, Morocco; College of Computing, Mohammed VI Polytechnic University, Ben Guerir 47963, Morocco; Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Pasto 520001, ColombiaFacultad de Ingeniería en Electricidad y Computación, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, EcuadorHospital Luis Vernaza de la Junta de Beneficencia de Guayaquil, Loja 700, Guayaquil 090313, EcuadorBiomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.http://www.sciencedirect.com/science/article/pii/S2352340923006406Brain–computer interfaceElectroencephalographyMotor taskMotor imagery taskOpenBCIExperimental methodology |
| spellingShingle | Víctor Asanza Leandro L. Lorente-Leyva Diego H. Peluffo-Ordóñez Daniel Montoya Kleber Gonzalez MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks Data in Brief Brain–computer interface Electroencephalography Motor task Motor imagery task OpenBCI Experimental methodology |
| title | MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks |
| title_full | MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks |
| title_fullStr | MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks |
| title_full_unstemmed | MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks |
| title_short | MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks |
| title_sort | milimbeeg a dataset of eeg signals related to upper and lower limb execution of motor and motor imagery tasks |
| topic | Brain–computer interface Electroencephalography Motor task Motor imagery task OpenBCI Experimental methodology |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923006406 |
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