Inner Speech

Inner Speech Dataset.

Author: Nicolás Nieto

Code available at: https://github.com/N-Nieto/Inner_Speech_Dataset

Publication available at: https://www.nature.com/articles/s41597-022-01147-2

Abstract:
Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces.
Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a “natural” way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-subjects dataset acquired under this and two others related paradigms, obtain with an acquisition systems of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.

Conditions = Inner Speech, Pronounced Speech, Visualized Condition

Classes = "Arriba/Up", "Abajo/Down", "Derecha/Right", "Izquierda/Left"

Total Trials = 5640

Please contact us at this e-mail address if you have any doubts: [e-mail]

内心语音数据集（Inner Speech Dataset）。作者：Nicolás Nieto。代码开源地址：https://github.com/N-Nieto/Inner_Speech_Dataset。论文发表地址：https://www.nature.com/articles/s41597-022-01147-2。

摘要：头皮脑电图（surface electroencephalography）是一种标准化且无创的脑电活动检测手段。近年来人工智能领域的进展显著提升了脑模式自动检测的性能，推动脑机接口（Brain-Computer Interfaces, BCI）朝着更快、更可靠、更易用的方向发展。学界已采用多种范式实现人机交互，且近年间对“内心语音”现象的解读与特征刻画研究兴趣显著提升。这种被称为内心言语（inner speech）的范式，为仅通过思维即可执行指令提供了可能，实现了一种“自然化”的外部设备控制方式。但当前公开可用的脑电数据集匮乏，限制了内心语音识别相关新技术的研发。本研究公开了一份基于该范式及另外两种相关范式采集的十受试者脑电数据集，采集系统包含136个通道。本研究的核心目标是为科学界提供一份开源的多分类内心语音指令脑电数据库，助力相关脑机制的深入研究。

实验范式（Conditions）：内心言语、出声言语、视觉化言语状态。类别（Classes）："Arriba/Up（上）"、"Abajo/Down（下）"、"Derecha/Right（右）"、"Izquierda/Left（左）"。总试次数（Total Trials）：5640。

如有任何疑问，请通过以下邮箱联系我们：[e-mail]

搜集汇总

背景与挑战

背景概述

该数据集是一个公开可用的多类脑电图（EEG）数据集，专注于内语言（inner speech）范式，用于脑机接口研究。它包含10名参与者在三种实验条件（内语言、发音语言、视觉化条件）下对'上/下/右/左'四个类别的5640次试验数据，采用136通道EEG采集系统，旨在促进内语言识别技术的发展和大脑机制的理解。