Deep convolutional neural network based character detection in devanagari script input based P300 speller

机译：基于深度卷积神经网络的字符检测在基于P300拼写器的devanagari脚本输入中

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摘要

P300 is a neuro-cognitive response to the brain elicits after visual stimulation. It has a prime importance in the development of p300-based brain-computer interface (BCI). Hence, accurate detection of p300 components will improve the performance of such BCI systems. There are various conventional machine learning techniques have been implemented for the p300 detection. However, the existing conventional techniques are incapable to handle high-dimensional and complex non-linear learning tasks. In such cases, deep learning techniques are the most reliable classification tools. Among them, deep convolution neural network (DCNN), a one of the powerful tool, has adopted in this work to classify the target and non-target p300 components from acquired EEG signal. The experimentation has been carried out on a self-generated dataset which was acquired using 16-channel V-amp EEG recorder. Experimental results illustrated that the proposed technique has achieved 94.18% accuracy for P300 detection which is higher than existing techniques.

机译：P300是视觉刺激后对大脑诱发的神经认知反应。它对基于p300的脑机接口（BCI）的开发至关重要。因此，准确检测p300组件将提高此类BCI系统的性能。对于p300检测，已经实现了各种常规的机器学习技术。但是，现有的常规技术无法处理高维和复杂的非线性学习任务。在这种情况下，深度学习技术是最可靠的分类工具。其中，深度卷积神经网络（DCNN）是一种功能强大的工具，已在这项工作中采用它从获取的EEG信号中对目标和非目标p300分量进行分类。实验是在一个自生成的数据集上进行的，该数据集是使用16通道V-amp EEG记录仪获取的。实验结果表明，所提出的技术对P300的检测精度达到了94.18％，高于现有技术。

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来源
《2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques》|2017年|507-511|共5页
会议地点 Mysuru(IN)
作者
Ghanahshyam B. Kshirsagar; Narendra D. Londhe;
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作者单位

Dept. of Electrical Engineering, National Institute of Technology Raipur, Raipur, C.G., India;

Dept. of Electrical Engineering, National Institute of Technology Raipur, Raipur, C.G., India;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Convolution; Electroencephalography; Neural networks; Training; Feature extraction; Machine learning; Testing;

机译：卷积;脑电图;神经网络;训练;特征提取;机器学习;测试;;

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1. Weighted Ensemble of Deep Convolution Neural Networks for Single-Trial Character Detection in Devanagari-Script-Based P300 Speller [J] . Kshirsagar Ghanahshyam B., Londhe Narendra D. IEEE Transactions on Cognitive and Developmental Systems . 2020,第3期

机译：基于Devanagari-Script的P300拼写的单次卷积神经网络的加权集合
2. Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning [J] . Kshirsagar G. B., Londhe N. D. IEEE Transactions on Biomedical Engineering . 2019,第11期

机译：使用深度学习提高基于Devanagari脚本输入的P300 Speller性能
3. Binary DE-Based Channel Selection and Weighted Ensemble of SVM Classification for Novel Brain-Computer Interface Using Devanagari Script-Based P300 Speller Paradigm [J] . Chaurasiya Rahul Kumar, Londhe Narendra D., Ghosh Subhojit International journal of human-computer interaction . 2016,第10a12期

机译：基于基于Devanagari脚本的P300 Speller范式的新型基于二元DE的通道选择和SVM分类加权集合，用于新型脑机接口
4. Deep convolutional neural network based character detection in devanagari script input based P300 speller [C] . Ghanahshyam B. Kshirsagar, Narendra D. Londhe International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques . 2017

机译：基于Devanagari脚本输入P300拼写的深度卷积神经网络的字符检测
5. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks [D] . Hassan, Abdalraouf. 2018

机译：基于卷积神经网络和递归神经网络的深度神经语言文本分类模型
6. Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts [O] . Rami Ahmed, Mandar Gogate, Ahsen Tahir, 2021

机译：基于新型卷积神经网络的阿拉伯语手写脚本的新型卷积神经网络
7. Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods [O] . Mahesh Jangid, Sumit Srivastava 2018

机译：使用深度卷积神经网络和自适应梯度方法的层面训练手写的Devanagari字符识别

Deep convolutional neural network based character detection in devanagari script input based P300 speller

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