EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Abstract

The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people’s lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used for window or floor cleaning. For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning, we extracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal. The visual stimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (e.g., 6 Hz, 7.5 Hz, 8.57 Hz, and 10 Hz). Seven subjects were able to smoothly control the movement directions of the cleaning robot by looking at the corresponding flicker using their brain activity. To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness.

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

“…The SSVEP-based BCI system also assists in reducing domestic pressure and improving home conditions by helping people accomplish heavy housework. Shao et al [36] designed a novel EEG-based intelligent teleoperation system for a mobile wall-crawling cleaning robot, which uses the crawler type instead of the traditional wheel type for window or floor cleaning. The developments of SSVEP-based BCI in smart environment field may offer the prospect of greatly improving the quality of life for disabled people out clinics, and considerably increase their independence, autonomy, mobility, and ability, which also leads to reduced social costs.…”

Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces

“…In addition to the DWT-based feature extraction BCI studies, DWT has been accepted as a common denoising method in other BCI studies in the literature [37][38][39][40][41][42]. To our knowledge, the mother wavelet comparison has been made for SSVEP by Zhang, Li, and Deng only.…”

“…Mishchenko et al[40] achieved the mean accuracy of 77% using SVM and 75% using LDA classifiers to discriminate three user commands from EEG signals. Shao et al[38] achieved the maximum accuracy of 89.92% using canonical correlation analysis (CCA) to classify four user commands from SSVEP signals after wavelet-based denoising. Similarly, Erkan and Akbaba[39] found the highest accuracy…”

Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

“…Among those forms, steady-state visual evoked potential (SSVEP) has attracted much attention due to the high communication rate, classification accuracy, and high signal-to-noise ratio (SNR) [ 6 , 7 ]. Driven by these advantages, the number of SSVEP-based real-time BCI applications have resulted in remarkable achievements [ 4 , 5 , 8 , 9 ].…”