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SSVEP-based Brain Computer Interface for 2-D cursor control: Comparative study of four classifiers.

机译：用于2D光标控制的基于SSVEP的脑计算机接口：四个分类器的比较研究。

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摘要

The present work was devoted to a comparative study of four classification algorithms targeting applications for Brain Computer Interface (BCI). BCI is a communication system that interprets brain signals (frequently, electrical activity) and converts them into control commands for an external environment. BCI can be viewed as a communication pathway for severely disabled individuals. Steady State Visual Evoked Potential (SSVEP) based BCI attract particular interest in today's research because of the possibility of a highly accurate and reliable BCI system with little or no user training.;SSVEP is an oscillatory response elicited by the brain that can be found in EEG and consists of fundamental frequency and number of harmonics corresponding to the frequency of visual stimulus – a flickering light, for instance, a phase reversal checkerboard pattern, or frequency modulated LEDs. The present study concerns a 2-dimensional cursor control application of BCI using SSVEP paradigm. Visual stimuli were implemented on the background of different colors to assess the effect of colors on the classification accuracy.;A spatial filter was designed using bipolar channel selection of EEG to enhance SSVEP. Four different methods were considered for detection and classification of SSVEP. Power spectrum-based approach using both parametric and non-parametric spectral estimators was explored as it is a widely used method for SSVEP-based BCI. Other methods, such as Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA), and Neural Network based detection and classification were considered. A comparison between these methods was performed based on classification accuracy, requirement of machine learning, possibility of high bit-rate, and computational complexity.;The power spectrum-based method was found impractical for designing the BCI system. CCA provides better accuracy than the power-spectrum approach and requires very few customizations for inter-subject variability, however, the accuracy is rather low. Higher accuracy was achieved using LDA classifier, which was found as more computationally efficient. Neural Network based classification provided the highest accuracy and was observed as a robust and reliable approach.
机译：目前的工作致力于比较针对脑计算机接口（BCI）应用的四种分类算法。 BCI是一种通信系统，可解释大脑信号（通常是电活动）并将其转换为外部环境的控制命令。 BCI可被视为严重残疾者的沟通途径。基于稳态视觉诱发电位（SSVEP）的BCI在当今的研究中引起了人们的特别关注，因为它可能具有高度准确且可靠的BCI系统，而无需或很少进行用户培训。脑电图由基本频率和与视觉刺激频率相对应的谐波数组成–闪烁的光，例如，相位反转棋盘图案或调频LED。本研究涉及使用SSVEP范例的BCI的二维光标控制应用程序。在不同颜色的背景上施加视觉刺激，以评估颜色对分类精度的影响。；利用EEG的双极通道选择设计空间滤波器以增强SSVEP。考虑了四种不同的方法用于SSVEP的检测和分类。由于它是基于SSVEP的BCI的一种广泛使用的方法，因此探索了同时使用参数和非参数频谱估计器的基于功率谱的方法。考虑了其他方法，例如规范相关分析（CCA），线性判别分析（LDA）和基于神经网络的检测和分类。根据分类精度，机器学习的要求，高比特率的可能性和计算复杂性对这些方法进行了比较。；发现基于功率谱的方法对于设计BCI系统不切实际。 CCA提供了比功率谱方法更好的准确性，并且几乎不需要针对对象间的可变性进行自定义，但是准确性非常低。使用LDA分类器可实现更高的精度，发现它的计算效率更高。基于神经网络的分类提供了最高的准确性，并被认为是一种强大而可靠的方法。

著录项

  • 作者

    Adil, Altaf.;

  • 作者单位

    Lamar University - Beaumont.;

  • 授予单位 Lamar University - Beaumont.;
  • 学科 Biology Neuroscience.;Engineering Electronics and Electrical.;Biology Bioinformatics.
  • 学位 M.S.
  • 年度 2011
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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