IIST BCI DATASET-6 FOR SELECTED COMMON ODIA WORDS

Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words. Using the OpenBCI Cyton device, EEG recordings are collected from a volunteer who speaks Odia language. The dataset is divided into 4 parts: (i) vocal Odia words, (ii) English translations of these Odia words, (iii) sub-vocalization of the Odia words, and (iv) sub-vocalization of English words. The dataset contains information about 100 different words. Each word is recorded with ten trials. By training the dataset using Machine Learning (ML) and Deep Learning (DL) methods, a BCI system can be designed to translate EEG signals into both vocal and subvocal for the Odia and English languages. This can enhance the communication and quality of Odia-speaking patients with neurodegenerative diseases.

脑机接口（Brain-Computer Interface，BCI）是一种可实现大脑与外部设备直接通信的技术，通常通过解读神经信号来达成通信目的。针对神经退行性疾病的BCI解决方案，需要采用包含患者母语信息的数据集。然而，BCI领域的研究仍存在针对性语言数据集不足的问题，例如在印度有3500万至4000万人使用的奥里亚语（Odia），其相关BCI数据集就存在明显缺口。为填补这一研究空白，我们构建了基于脑电图（Electroencephalograph，EEG）的BCI数据集，该数据集收录了常用奥里亚语词汇的脑电信号样本。我们使用OpenBCI Cyton设备，从一名母语为奥里亚语的志愿者身上采集了脑电记录数据。本数据集共分为四个部分：(i) 出声朗读的奥里亚语词汇，(ii) 上述奥里亚语词汇的英文译文，(iii) 奥里亚语词汇的无声默念，(iv) 英文词汇的无声默念。该数据集涵盖100个不同的词汇，每个词汇均完成了10次试次采集。通过使用机器学习（Machine Learning，ML）与深度学习（Deep Learning，DL）方法对该数据集进行训练，可构建出一套BCI系统，能够将脑电信号转换为奥里亚语与英语的出声语音及无声默念内容。这将有效提升患有神经退行性疾病的奥里亚语使用者的沟通能力与生活质量。