Khasnobish A, Datta S, Bose R, Tibarewala D N, Konar A
TCS Innovation Labs, New Town, Kolkata, 700156 India.
Electrical Engineering Department, Calcutta Institute of Engineering and Management, 24/1A, Chandi Ghosh Road, Kolkata, 700040 India.
Cogn Neurodyn. 2017 Dec;11(6):501-513. doi: 10.1007/s11571-017-9452-2. Epub 2017 Sep 6.
Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets. Classifiers based on the principle of supervised learning are trained on the extracted EEG feature space, comprising three features, namely, adaptive autoregressive parameters, Hurst exponents, and power spectral density, to recognize the respective texts. The pre-trained classifiers are used to classify the EEG data to identify the texts online and the recognized text is displayed on the computer screen for communication. Online classifications of two, four, and six classes of embossed texts are achieved with overall average recognition rates of 76.62, 72.31, and 67.62% respectively and the computational time is less than 2 s in each case. The maximum information transfer rate and utility of the system performance over all experiments are 0.7187 and 2.0529 bits/s respectively. This work presents a study that shows the possibility to classify 3D letters using tactually evoked EEG. In future, it will help the BCI community to design stimuli for better tactile augmentation n also opens new directions of research to facilitate 3D letters for visually impaired persons. Further, 3D maps can be generated for aiding tactual BCI in teleoperation.
对物体的触觉探索会在人脑中产生特定模式，因此可以通过在触觉探索过程中分析脑信号来识别物体。目前的工作旨在在线分析脑电图（EEG）信号，以便通过触觉探索识别压纹文本。在蒙眼的健康受试者通过触觉探索包括符号、数字和字母在内的压纹文本时，从体感皮层顶叶区域采集EEG信号。基于监督学习原理的分类器在提取的EEG特征空间上进行训练，该特征空间包括三个特征，即自适应自回归参数、赫斯特指数和功率谱密度，以识别相应的文本。预训练的分类器用于对EEG数据进行分类，以在线识别文本，并将识别出的文本显示在计算机屏幕上进行交流。实现了对两类、四类和六类压纹文本的在线分类，总体平均识别率分别为76.62%、72.31%和67.62%，并且每种情况下的计算时间均小于2秒。在所有实验中，系统性能的最大信息传输率和效用分别为0.7187和2.0529比特/秒。这项工作展示了一项研究，表明利用触觉诱发的EEG对三维字母进行分类的可能性。未来，它将帮助脑机接口（BCI）社区设计刺激，以实现更好的触觉增强，也为促进视障人士使用三维字母开辟了新的研究方向。此外，可以生成三维地图，以辅助远程操作中的触觉BCI。
