Optimizing Prediction Model for a Noninvasive Brain-Computer Interface Platform Using Channel Selection, Classification, and Regression
Optimizing Prediction Model for a Noninvasive Brain-Computer Interface Platform Using Channel Selection, Classification, and Regression
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DOI：
10.1109/jbhi.2019.2892379
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发表时间：
2019-11-01
影响因子：
7.7
通讯作者：
Zhao, Xiaopeng
中科院分区：
文献类型：
--
作者：
Borhani, Soheil;Kilmarx, Justin;Zhao, Xiaopeng
脑机接口（BCI）平台可以由用户用来控制外部设备而不进行任何明显的移动。基于EEG的计算机光标控制任务通常用作BCI应用的测试平台。虽然传统的计算机光标控制方案是基于感觉运动节奏，最近开发了一种新的方案，使用想象的身体运动学（IBK），以实现自然的光标运动在较短的时间内的培训。本文试图探索最佳的解码算法的IBK范式使用脑电信号与应用程序的神经光标控制。该研究基于对32名健康受试者的训练数据的离线分析。实施了各种机器学习技术，以在训练任务期间使用EEG信号来预测计算机光标的运动学。我们的研究结果表明，线性回归最小二乘模型产生了最高的拟合优度得分的光标运动学模型（70水平预测和40垂直预测使用Theil-Sen回归）。此外，每个EEG通道对光标运动学的可预测性的贡献分别针对水平和垂直方向进行了检查。还提出了一种方向分类器，利用EEG信号对水平和垂直光标运动进行分类。通过结合从特定频带中提取的特征，我们在区分水平和垂直光标移动方面达到了80的分类准确度。目前的研究结果可以促进设计优化的在线神经光标控制的途径。
A brain-computer interface (BCI) platform can be utilized by a user to control an external device without making any overt movements. An EEG-based computer cursor control task is commonly used as a testbed for BCI applications. While traditional computer cursor control schemes are based on sensorimotor rhythm, a new scheme has recently been developed using imagined body kinematics (IBK) to achieve natural cursor movement in a shorter time of training. This article attempts to explore optimal decoding algorithms for an IBK paradigm using EEG signals with application to neural cursor control. The study is based on an offline analysis of 32 healthy subjects' training data. Various machine learning techniques were implemented to predict the kinematics of the computer cursor using EEG signals during the training tasks. Our results showed that a linear regression least squares model yielded the highest goodness-of-fit scores in the cursor kinematics model (70 in horizontal prediction and 40 in vertical prediction using a Theil-Sen regressor). Additionally, the contribution of each EEG channel on the predictability of cursor kinematics was examined for horizontal and vertical directions, separately. A directional classifier was also proposed to classify horizontal versus vertical cursor kinematics using EEG signals. By incorporating features extracted from specific frequency bands, we achieved 80 classification accuracy in differentiating horizontal and vertical cursor movements. The findings of the current study could facilitate a pathway to designing an optimized online neural cursor control.