- Decoding Individual Finger Movements with g.Pangolin Ultra HD-EEG
Decoding Individual Finger Movements with g.Pangolin Ultra HD-EEG
Brain-Computer Interface (BCI) technology enables users to control external devices without physical movement. Electroencephalography (EEG) based BCI-systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living.
In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average.
Rehabilitating motor functions of the hand, especially fingers, is essential for improving activities of daily living (ADLs) for people who experience upper limb motor impairment. Finger movements are required to manipulate tools, grab and move objects, and make signs, which are functions repeatedly used and indispensable. However, decoding individual finger movements are mostly performed with invasive methods such as ECoG. This limitation demands using non-invasive methods, such as electroencephalography (EEG).
EEG acquires electrophysiological signals from the scalp. Since EEG does not require clinical surgery and offers a high temporal resolution with low price and high portability, it is the most widely used among various brain signal acquisition methods. Moreover, these characteristics empower EEG-based BCI systems to be used as real-time closed-loop applications, the final goal of BCI application systems.
Decoding more precise movements, such as individual finger movements, is highly challenging for conventional EEG systems. It has low spatial resolution caused by common EEG electrodes’ large layout and size and its susceptibility to artifacts that often originate from movement and electromyographic (EMG) activity. Due to its limitations, the current state-of-the-art EEG systems only allow the distinction of gross movements. For example, research was done to discriminate hand grasp and release, right and left-hand movements, upper-limb movements, and lower-limb movements.