A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection

Abstract

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications

“…The recording system consists of dry electrodes with two spring-loaded silver pins per electrode (see also 19,22,32 for similar solutions), The pins are available in two different lengths (12 and 15 mm) to accommodate different head shapes and hair volume, thereby avoiding the need of a chin strap. The two-pins per electrode set up is in line with findings of a previous study 32 that systematically evaluated various dry electrode designs differing in the number of pins (referred to as 'fingers' in their paper) per electrode.…”

“…(2020) 10:5218 | https://doi.org/10.1038/s41598-020-62154-0www.nature.com/scientificreports www.nature.com/scientificreports/ by the aforementioned criteria and omitting the high pass and notch filtering mentioned above. Absolute spectral band power values were computed for the following frequency bands, which are typically reported in clinical EEG settings: Delta (1.5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz), Beta(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30).…”

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

“…BCI2000 is in charge of controlling the virtual cursor and displaying the virtual targets. In EEG-based BCI systems, the use of spatial filters can significantly improve the user's performance [20]. A CAR filter was used to preprocess the eight-channel motor imagery EEG signals.…”

“…Then, an autoregressive (AR) model was adopted to estimate the amplitude of the sensorimotor rhythm in a specific frequency band of the subject. The AR model is given by: In EEG-based BCI systems, the use of spatial filters can significantly improve the user's performance [20]. A CAR filter was used to preprocess the eight-channel motor imagery EEG signals.…”

“…However, few research groups have attempted to apply the motor imagery EEG signals to the teleoperation robotic system in reach and grasp tasks, and many difficulties still remain, such as continuous extracting trajectory information from human movement imagination, continuous multidimensional control, and tactile feedback. Most of the previous research focuses on the conventional two class or four class classification of EEG signal to get one or four discrete control commands and trigger the robot to move along the predefined trajectory instead of directly converting the EEG signals to multidimensional continuous control information [20]. For example, Bousseta et al [21] proposed a BCI system which controlled a robot arm based on the user's thought.…”