ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters
This dataset addresses a gap for Arabic-speaking individuals with disabilities, but it is incremental as it applies an existing method to new data.
The authors tackled the lack of publicly available EEG datasets for non-English languages by introducing ArEEG_Chars, a novel EEG dataset for 31 Arabic characters collected from 30 participants, resulting in 39,857 EEG recordings.
Brain-computer interfaces is an important and hot research topic that revolutionize how people interact with the world, especially for individuals with neurological disorders. While extensive research has been done in EEG signals of English letters and words, a major limitation remains: the lack of publicly available EEG datasets for many non-English languages, such as Arabic. Although Arabic is one of the most spoken languages worldwide, to the best of our knowledge, there is no publicly available dataset for EEG signals of Arabic characters until now. To address this gap, we introduce ArEEG_Chars, a novel EEG dataset for Arabic 31 characters collected from 30 participants (21 males and 9 females), these records were collected using Epoc X 14 channels device for 10 seconds long for each char record. The number of recorded signals were 930 EEG recordings. To make the EEG signals suitable for analyzing, each recording has been split into multiple signals with a time duration of 250ms, respectively. Therefore, a total of 39857 recordings of EEG signals have been collected in this study. Moreover, ArEEG_Chars will be publicly available for researchers. We do hope that this dataset will fill an important gap in the research of Arabic EEG benefiting Arabic-speaking individuals with disabilities.