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.

/pdf/eeg-controlled-wall-crawling-cleaning-robot-using-ssvep-54w7ft8463.pdf

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

The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1) A hardware device for the acquisition and transmission of the signals generated by eye movement to the computer; (2) A software application that allows, among other functions, data processing in order to minimize noise and classify signals; and (3) A graphical interface that allows the user to write text easily on the computer screen using eye movements only. This work analyzes these three subsystems and proposes innovative and low cost solutions for each one of them. This computer writing system was tested with 20 users and its efficiency was compared to a traditional virtual keyboard. The results have shown an important reduction in the time spent on writing, which can be very useful, especially for people with severe motor disorders.

https://www.mdpi.com/1424-8220/17/7/1505/pdf

Development of a Computer Writing System Based on EOG.

Scene text detection and recognition: a survey

Ataxias are a group of neurodegenerative disorders characterized by cerebellar dysfunction that cause irregularities in the rate, rhythm, amplitude, and force of voluntary movements. The electrooculogram (EOG) may provide clues about ataxic disorders because most of these patients have difficulty with visual tracking and fixing their gaze. Using electrodes, EOG records the biopotentials generated by eye movements. In this paper, three surface electrodes are placed around the eye socket, and the biopotentials generated by eye movements are acquired using a commercial bioamplifier device. Next, the signals are sent to the computer to be digitally processed to extract the rate of saccades as well as the delay and deviation of the gaze in response to a stimulus. These features are analysed in a novel software application designed to help physicians in evaluating ataxia. After applying several tests to both healthy and ataxia-affected patients, differences in EOG results were found. The evaluation of the reliability of the designed software application is made according to three metrics: sensitivity, specificity, and accuracy. The results indicate the proposed system’s viability as an affordable method for evaluation of ataxic disorders.

An Affordable Method for Evaluation of Ataxic Disorders Based on Electrooculography

Unlike existing models that aim to address the challenge of scene text detection and recognition separately, the proposed work aims to address both text detection and recognition using a single architecture to deal with arbitrarily oriented/shaped text. Towards this aim, a novel Text Proposal with Location-Awareness-Attention Network (TPLAANet) for arbitrarily oriented/shaped text detection and recognition is proposed. For text detection, the proposed method explores central mask prediction for locating text instances, bounding box regression branch for tight bounding boxes, and mask branch for accurate positions of arbitrarily oriented/shaped text instances. For text recognition, the proposed method explores character information using a Location-Awareness-Attention Network (LAAN), which learns a two-dimensional attention weight and improves the recognition performance. To test the efficacy of the proposed model, we consider the commonly used horizontal and multi-oriented natural scene text datasets, namely, ICDAR2013, ICDAR2015, and the arbitrarily shaped scene text datasets, namely, Total-Text and CTW1500 for experimentation. Experimental results are provided to validate the effectiveness of the proposed method. The code is available at: https://codeocean.com/capsule/5666319/tree/v1. 

Text proposals with location-awareness-attention network for arbitrarily shaped scene text detection and recognition

Detection and recognition of text from any natural scene image is challenging but essential extensively for extracting information from the image. In this paper, we propose an accurate and effective algorithm for detecting enhanced Maximally Stable Extremal Regions (MSERs) as main character candidates and these character candidates are filtered by stroke width variation for removing regions where the stroke width exhibits too much variation. For the detection of text regions, firstly some preprocessing is applied to the natural image and then after detecting MSERs, an intersection of canny edge and MSER region is produced to locate regions that are even more likely to belong to text. Finally, the selected text region is taken as an input of a novel Optical Character Recognition (OCR) technique to make the text editable and usable. The evaluation results substantiates 77.47% of the f-measure on the ICDAR 2011 dataset which is better than the previous performance 76.22%.

Text detection and recognition using enhanced MSER detection and a novel OCR technique

Most of the paralyzed or physically disabled persons are unable to communicate with others, easily. To minimize this problem, different types of Human-Computer Interface (HCI) systems have been developed in recent years. In this paper, a single channel electrooculography (EOG) based HCI system has been proposed to increase the communication ability as well as quality of life for paralyzed persons who cannot speak or move their limbs. The extracted EOG signals are processed by our EOG acquisition system and sent it to a microcontroller unit which processes those signals for interfacing with computer via serial communication. A Graphical User Interface (GUI) is designed using MATLAB which contains some buttons to help a user to express what he/she wants through messages. A liquid crystal display (LCD) is used to show the messages. Our experimental results show that the maximum and minimum average time recorded for selecting 10 buttons for a particular user are 4.27 and 4.11 second, respectively. Particularly for selecting a button, the maximum and minimum average time recorded by every user are respectively 5.58 second and 1.82 second. We have found that the average button selection accuracy is around 95%.

Single channel electrooculography based Human-Computer Interface for physically disabled persons

For different controlling applications, tracking of eye movements is becoming increasingly popular nowadays. Particularly it is very helpful for the disabled people suffering from Amyotrophic Lateral Sclerosis (ALS) or other illness that prevent correct response of their limbs as well as deprive of their ability to speak. Among different eye movements tracking methods, in this paper Electrooculography (EOG) based system is developed. Electrooculogram (EOG) signals can be used to improve the communication ability as well as the quality of life of the disabled persons. Some devices are made for the purpose of lab experiments to take EOG signal for different patterns of eye movement. A typical EOG data acquisition device or circuit should be developed and compared the data to the standard device one. In this paper a low cost EOG data acquisition system is proposed. Different patterns of eye movement are recorded by the proposed EOG acquisition device and compared them with the standard BIOPAC MP36 system to check accuracy of our designed system. It will play a vital role to develop advanced devices and real life applications for paralyzed or physically disabled persons those who are affected by different diseases of any organ of the body except eye. Compared to standard device, our proposed scheme works very well with less error.

Design and implementation of a prototype Electrooculography based data acquisition system

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Text detection and recognition using enhanced MSER detection and a novel OCR technique

Single channel electrooculography based Human-Computer Interface for physically disabled persons

Design and implementation of a prototype Electrooculography based data acquisition system