EEG‐Based Discrimination of Imagined Speech Phonemes

Xuemin Chi; John B. Hagedorn; Daniel Schoonover; Michael DZmura

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EEG‐Based Discrimination of Imagined Speech Phonemes

机译：基于EEG的想象语音音素区分

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This paper reports positive results for classifying imagined phonemes on the basis of EEG signals. Subjects generated in imagination five types of phonemes that differ in their primary manner of vocal articulation during overt speech production (jaw, tong

机译：本文报告了基于脑电信号对假想音素进行分类的积极结果。主体在想象中生成了五种音素，它们在公开语音产生过程中的主要发声方式不同（下颚，钳子）

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《International Journal of Bioelectromagnetism》 |2011年第4期|共6页
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Xuemin Chi; John B. Hagedorn; Daniel Schoonover; Michael DZmura;
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中图分类生物电磁学;
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1. EEG‐Based Discrimination of Imagined Speech Phonemes [J] . Xuemin Chi, John B. Hagedorn, Daniel Schoonover, International Journal of Bioelectromagnetism . 2011,第4期

机译：基于EEG的想象语音音素区分
2. Phoneme analysis and phoneme discrimination of juvenile speech therapy school students [J] . Franz S, Rosanowski F, Eysholdt U, Laryngo- rhino- otologie . 2011,第5期

机译：少年语言治疗学校学生的音素分析与音素辨别
3. Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition [J] . Jimenez-Guarneros Magdiel, Gomez-Gil Pilar Pattern recognition letters . 2021,第Jana期

机译：在想象语音识别中基于跨对象EEG的分类的标准化 - 精细域适应方法
4. Tensor Decomposition for Imagined Speech Discrimination in EEG [C] . Jesus S. Garcia-Salinas, Luis Villasenor-Pineda, Carlos Alberto Reyes-Garcia, Mexican international conference on artificial intelligence . 2018

机译：脑电中语音想象歧视的张量分解
5. Improved phoneme-based myoelectric speech recognition. [D] . Zhou, Quan. 2008

机译：改进的基于音素的肌电语音识别。
6. Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG [O] . Ciaran Cooney, Attila Korik, Raffaella Folli, 2020

机译：解码中的机器和深层学习方法中的高参数优化评估
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

EEG‐Based Discrimination of Imagined Speech Phonemes

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