Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

A comprehensive review of EEG-based brain-computer interface paradigms.

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Medical Instrumentation Application And Design

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

https://iopscience.iop.org/article/10.1088/1741-2560/11/5/051001/pdf

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

In this paper, hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near-infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), eye tracker, etc. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy, and reduce the signal detection time. Currently, such combinations of EEG+fNIRS and EEG+EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active BCI accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP), and motor imagination with P300, etc. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

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Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this paper, we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, and visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across 20 participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (four channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate across six participants. This paper demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.

/pdf/a-high-performance-spelling-system-based-on-eeg-eog-signals-3ejtedo6l3.pdf

A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback

Abstract In the last few decades, bio-robotics applications such as exoskeletons, prosthetics and robotic wheelchairs have progressed from machines in science fiction to nearly commercialized products. Though there are still several challenges associated with electromyography (EMG) signals, the advances in use of EMG signals for controlling such bio-robotics applications have been enormous. Similarly, recent trends and attempts in developing electroencephalography- (EEG) based control methods have shown the potential of this area in the modern bio-robotics field. However, the EEG-based control methods are also yet to be perfected. A new approach of combining both these control methods, which take the advantages, and diminish the disadvantages, of each system might therefore be a promising approach. In this paper, we review hybrid fusion of EMG- and EEG-based control approaches in the bio-robotics field which have been attempted or developed to date. We provide a design overview of the method and consider the main features and merits/disadvantagages for the approaches that have been analyzed. We also discuss the current challenges regarding these hybrid EEG-EMG control approaches and propose some potential future directions.

/pdf/towards-hybrid-eeg-emg-based-control-approaches-to-be-used-8080top7rv.pdf

Towards Hybrid EEG-EMG-Based Control Approaches to be Used in Bio-robotics Applications: Current Status, Challenges and Future Directions

It may be difficult task for physically weak elderly, disabled and injured individuals to perform the day to day activities in their life Therefore, many assistive devices have been developed in order to improve the quality of life of those people Especially upper-limb power-assist exoskeletons have been developed since the upper limb motions are vital for the daily activities Electromyography (EMG) signals of the upper limb muscles have sometimes been used as a primary signal to control the power assist exoskeletons since the EMG signals directly reflect the motion intention of the user But one of the main obstacles for EMG based controller is the muscle fatigue, because the muscle fatigue might change the EMG patterns It is important for power-assist exoskeleton to correctly assist the user for longer period of time But it has high probability of user muscles been fatigued because users getting more and more exhausted at the end of the day Therefore it is necessary to consider the variations of EMG signals due to the effect of muscle fatigue In this paper it demonstrates the study which was conducted to find out the effects of muscle fatigue on the three EMG features derived from the raw EMG signals of the Bicep brachii, Deltoid-posterior, Deltoid-anterior and Supinator muscles of the upper limb Shoulder vertical flexion/extension, shoulder abduction/adduction, elbow flexion/extension and forearm pronation/supination motions were carried out before and after a set of muscle fatiguing exercises The three features computed in this experiment were RMS (Root Mean Square), MPF (Mean Power Frequency) and a spectral feature (FInsm5) which was proposed by Dimitrov Comparison results of these three features of all muscles before and after the fatiguing exercises showed an percentage increase of the RMS and FInsm5 features whereas MPF showed a percentage decrease with respect to the before fatiguing conditions The result showed that the EMG RMS may not a reliable feature to use as the only input signal in EMG based control for human upper-limb power assist in the muscle fatiguing conditions Therefore, it is suggested that a modification method for compensating the effect of muscle fatigue is required on the EMG based control in order to have a long and reliable use of the human upper-limb power assist exoskeletons

A study on effects of muscle fatigue on EMG-based control for human upper-limb power-assist

Need of assistive robots for performing activities of daily living is increasing with the reduction of labor force in the welfare and nursing care. Self-feeding or eating is one of the primary activities of a human in his/her daily life. Devices such as assistive robots for self-feeding have been developed as a solution for this problem. The purpose of this paper is to review existing meal assistance robots. In the paper, identification of important design features like feeding techniques, advantages and limitations of control methods of meal assistance robots and different inputs signals are comprehensively discussed. Challenges for developing meal assistance robots and potential future directions are also discussed at the end.

Meal assistance robots: A review on current status, challenges and future directions

A Meal Assistance Robot is an assistive device that is used to aid individuals who cannot independently direct food to their mouths for consuming. For individuals who undergo loss of upper limb functions due to amputations, spinal cord injuries or cerebral palsy, self-feeding can be impossible, and to assist such individuals in regaining their independence meal assistance robots have been introduced. In this paper we propose a meal assistance robot that is controlled using user intentions based on Electroencephalography (EEG) signals while incorporating camera-based automatic mouth position tracking and mouth open detection systems. In the proposed system, users select any solid food item that they desire to consume from three different containers by looking at corresponding flickering LED matrices. User intentions are identified through EEG signals using a Steady State Visual Evoked Potentials (SSVEP) based intention detection method. Initial motion commands for scooping food from the containers are generated and sent to the meal assistance robot from this first stage. At the second stage, a camera-based mouth position tracking method is proposed for automatically detecting the user's mouth position and thereby moving the spoon or end-effector of the meal assistance robot towards the mouth of the user. This method is capable of automatically tracking the mouth position of users irrespective of their individual body differences and seating positions. A mouth open/closed recognition method is implemented at the final stage in order to feed food to the users when they desire consumption, indicated by the opening/closing of their mouth. A set of experiments were carried out with healthy subjects to validate the proposed system and results are here presented.

EEG-controlled meal assistance robot with camera-based automatic mouth position tracking and mouth open detection

Meal assistance robots provide disabled individuals the access to one of the important activities in daily living, self-feeding. This paper proposes a Steady State Visually Evoked Potential (SSVEP) based Brain Machine Interface (BMI) for controlling of a meal assistance robot. In the proposed system, the user has the facility to select any solid food item that he would like to eat from 3 different bowls just by looking at the respective LED matrices blinking at different frequencies. The generated SSVEP signals while looking at the LEDs are extracted from EEG signals acquired using OpenBCI EEG signal acquisition system. Extracted SSVEP signals are used to identify the intention of the user and subsequently the detected intentions are used to operate the meal assistant robot. Experiments are carried out to validate the system and results indicate the effectiveness of the proposed method.

Thilina Dulantha Lalitharatne

Papers

Towards Hybrid EEG-EMG-Based Control Approaches to be Used in Bio-robotics Applications: Current Status, Challenges and Future Directions

A study on effects of muscle fatigue on EMG-based control for human upper-limb power-assist

Meal assistance robots: A review on current status, challenges and future directions

EEG-controlled meal assistance robot with camera-based automatic mouth position tracking and mouth open detection

SSVEP based BMI for a meal assistance robot