DBCIE: A Database for Brain-Computer Interface Using Electrophysiological Signal

This paper presents a database for electrophys-iological signal, specifically the electroencephalogram (EEG). Primarily, the database is a repository of EEG data collected in our laboratory. The signals were collected in three different scenarios: left-arm (LA) movement, right-arm (RA) movement, and simultaneous movement of both arms (BA). 22 electrodes of an EEG system are positioned across the scalp on the head to capture brain electrical activity, as per the 10–20 standard protocol. All EEG signals are recorded using three placement methods, namely Montage 1, Montage 2, and Montage 3. This is an advantage over previously available datasets, where only one configuration was used for data acquisition. The primary objective of EEG data acquisition for classifying motor imagery (MI) tasks is to build a brain-computer interface (BCI) system. In this work, we evaluate and discuss some statistical features, such as the mean, standard deviation, kurtosis, skewness, energy, root mean square (RMS) value, variance, and median of 23 medically normal participants. In addition to extracting features, a comparative analysis of these features is presented, taking into account the EEGs of LA, RA, and BA movements using a one-way analysis of variance (ANOVA) test. Hence, the proposed method of comparing the features of three classes of EEGs can effectively distinguish individuals performing MI tasks from their counterparts. We invite other researchers to use the dataset to test their own approaches for the study and/or detection of the physiology and pathology of the human brain. The data is annotated with the following information: (a) metadata of the subject, age, gender, medical history, current neurological and motor status, date and time of recording, and (b) conditions of data recording, like a type of hand movement. The dataset shall be made publicly available.