Abstract
Brain-Computer Interface (BCI) based on Local Field Potential (LFP) has recently been developed to restore communication or behavioral functions. LFP provides comprehensive information, due to its stability, robustness, and reach frequency content within the cognitive process. It has been demonstrated that spatial attention can be decoded from brain activity in the visual cortical areas. However, whether motion direction can be decoded from the LFP signal in the primate visual cortex remains uninvestigated, as well as how decoding performance may be influenced by spatial attention. In this paper, these issues were examined by recording LFP from the middle temporal area (MT) of macaque, employing machine learning algorithms. The animal was trained to report a brief direction change in a target stimulus which moved in various directions during a visual attention task. It was found that the LFP-gamma power was able to provide significant information to reliably decode motion direction, compared with other frequency bands, on a single-trial basis. Moreover, the results show that spatial attention leads to enhancements in motion direction discrimination performance. The highest decoding performance was achieved in the high-gamma frequencies (60–120Hz) when targets were presented inside the receptive field in opposite directions. Using a feature selection approach, performance was improved by optimally selecting features where the highest level of participation was observed in the gamma-band. Generally, the results suggest that in the MT area, LFP signals exhibit appreciable information about visual features like motion direction, which could thus be utilized as a control signal for cognitive BCI systems.
Highlights
Visual attention is a selective mechanism which aims to prioritize behaviorally relevant information out of all subsets of sensory stimulation
The feasibility of the motion direction decoding was evaluated based on Local Field Potential (LFP) signals recorded from the middle temporal area (MT) area of a macaque monkey in a target change-detection task
This study investigated whether it was possible to decode target motion direction with reliable performance by using LFP recorded from the MT visual cortex
Summary
Visual attention is a selective mechanism which aims to prioritize behaviorally relevant information out of all subsets of sensory stimulation. It focuses on spatial positioning (spatial attention) or target-defining features (feature-based attention) across the visual cortex [1], [2]. Visual attention as a cognitive factor plays a substantial role in the higher-order mental information processing that occurs in the brain [3], [4]. BCI technology is a computer-based system that decodes and interprets brain signals in order to control an external. Numerous studies have suggested covert visual attention in cognitive BCI applications [9]–[11]
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