EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks
EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks
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DOI：
10.1016/j.bspc.2021.102507
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发表时间：
2021-02
影响因子：
5.1
通讯作者：
Ozan Ozdenizci;Safaa M. Eldeeb;Andac Demir;Deniz Erdoğmuş;M. Akçakaya
中科院分区：
文献类型：
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作者：
Ozan Ozdenizci;Safaa M. Eldeeb;Andac Demir;Deniz Erdoğmuş;M. Akçakaya
在日常活动中，人类用手抓住周围的物体并感知感官信息，这些信息也用于感知和运动目标。在感觉运动加工过程中，多个大脑皮层区域负责感觉识别、感知和运动执行。虽然各种研究特别关注人类感觉运动控制领域，但运动执行和感觉处理之间的关系和处理尚未完全理解。我们工作的主要目标是在主动触觉探索过程中使用同时记录的脑电图（EEG）数据来区分其粗糙度水平不同的纹理表面，同时最大限度地减少不同的运动探索运动模式的方差。我们进行了一项实验研究，8名健康的参与者被指示使用他们的优势手食指的尖端，同时摩擦或敲击三个不同的粗糙度的纹理表面。我们使用对抗性不变表示学习神经网络架构，该架构对不同纹理表面执行基于EEG的分类，同时最小化运动条件的可辨别性（即，摩擦或轻拍）。结果表明，所提出的方法可以区分三种不同的纹理表面的准确度高达70%，同时抑制运动相关的变化从学习表示。
During daily activities, humans use their hands to grasp surrounding objects and perceive sensory information which are also employed for perceptual and motor goals. Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing. While various research studies particularly focus on the domain of human sensorimotor control, the relation and processing between motor execution and sensory processing is not yet fully understood. Main goal of our work is to discriminate textured surfaces varying in their roughness levels during active tactile exploration using simultaneously recorded electroencephalogram (EEG) data, while minimizing the variance of distinct motor exploration movement patterns. We perform an experimental study with eight healthy participants who were instructed to use the tip of their dominant hand index finger while rubbing or tapping three different textured surfaces with varying levels of roughness. We use an adversarial invariant representation learning neural network architecture that performs EEG-based classification of different textured surfaces, while simultaneously minimizing the discriminability of motor movement conditions (i.e., rub or tap). Results show that the proposed approach can discriminate between three different textured surfaces with accuracies up to 70%, while suppressing movement related variability from learned representations.