Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation

This paper presents a comprehensive review of high-level control techniques for upper-limb robotic training. It aims to compare and discuss the potentials of these different control algorithms, and specify future research direction. Included studies mainly come from selected papers in four review articles. To make selected studies complete and comprehensive, especially some recently-developed upper-limb robotic devices, a search was further conducted in IEEE Xplore, Google Scholar, Scopus and Web of Science using keywords (‘upper limb*’ or ‘upper body*’) and (‘rehabilitation*’ or ‘treatment*’) and (‘robot*’ or ‘device*’ or ‘exoskeleton*’). The search is limited to English-language articles published between January 2013 and December 2017. Valuable references in related publications were also screened. Comparative analysis shows that high-level interaction control strategies can be implemented in a range of methods, mainly including impedance/admittance based strategies, adaptive control techniques, and physiological signal control. Even though the potentials of existing interactive control strategies have been demonstrated, it is hard to identify the one leading to maximum encouragement from human users. However, it is reasonable to suggest that future studies should combine different control strategies to be application specific, and deliver appropriate robotic assistance based on physical disability levels of human users

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

[EN] Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Staubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Staubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.Funding for open access charge: Universitat Politecnica de Valencia.Quiles Cucarella, E.; Dadone, J.; Chio, N.; García Moreno, E. (2022). Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm. Sensors. 22(13):1-26. https://doi.org/10.3390/s22135000126221

Assisting Drinking With an Affordable BCI-Controlled Wearable Robot and Electrical Stimulation: A Preliminary Investigation

Background The aim of the present study is to demonstrate, through tests with healthy volunteers, the feasibility of potentially assisting individuals with neurological disorders via a portable assistive technology for the upper extremities (UE). For this purpose the task of independently drinking a glass of water was selected, as it is one of the most basic and vital activities of the daily living that is unfortunately not achievable by individuals severely affected by stroke. Methods To accomplish the aim of this study we introduce a wearable and portable system consisting of a novel lightweight Robotic Arm Orthosis (RAO), a Functional Electrical Stimulation (FES) system, and a simple wireless Brain-Computer Interface (BCI). This system is able to process electroencephalographic (EEG) signals and translate them into motions of the impaired arm. Five healthy volunteers participated in this study and were asked to simulate stroke patient symptoms with no voluntary control of their hand and arm. The setup was designed such as the volitional movements of the healthy volunteers’ UE did not interfere with the evaluation of the proposed assistive system. The drinking task was split into eleven phases of which seven were executed by detecting EEG-based signals through the BCI. The user was asked to imagine UE motion related to the specific phase of the task to be assisted. Once detected by the BCI the phase was initiated. Each phase was then terminated when the BCI detected the volunteers clenching their teeth. Results The drinking task was completed by all five participants with an average time of 127 seconds with a standard deviation of 23 seconds. The incremental motions of elbow extension and elbow flexion were the primary limiting factors for completing this task faster. The BCI control along with the volitional motions also depended upon the users pace, hence the noticeable deviation from the average time. Conclusion Through tests conducted with healthy volunteers, this study showed that our proposed system has the potential for successfully assisting individuals with neurological disorders and hemiparetic stroke to independently drink from a glass

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed

Development of an EEG-based recurrent neural network for online gait decoding

Recent neuroscientific literature has shown that the use of brain-controlled robotic exoskeletons in walking rehabilitation induces neuroplasticity modi- fications, possibly leading to a higher likelihood of recovery and maintenance of lost motor functions due to a neural lesion, with respect to traditional re- habilitation. However, the gait decoding from brain signals remains an open challenge. The aim of this work is to implement and validate a deep learning model for online gait decoding that exploits Electroencephalography (EEG) infor- mation to predict the intention of initiating a step, which could be used to trigger the assistance of a lower-limb exoskeleton. In particular, the model exploits a Gated Recurrent Units (GRU) deep neural network to handle the time-dependent features which were identified by analysing the neural cor- relates preceding the step onset (i.e., Movement-Related Cortical Potentials (MRCP)). The network was evaluated on a pre-recorded dataset of 11 healthy subjects walking on a treadmill. The network’s architecture (e.g., number of GRU units) was optimized through grid search. In addition, to deal with the data scarcity problem of neurophysiological applications, I proposed a data augmentation procedure to increase the dataset available to train the model of each subject. With the proposed approach, the model achieved an average accuracy in detecting the step onset of 89.7 ± 7.7% with just the 15% of the dataset for each subject (∼70 steps), and up to 97.8 ± 1.3% with the whole dataset (∼440 steps). This thesis support the use of a memory-based deep learning model to de- code walking activity from non-invasive brain recordings. In future works, this model will be exploited in real time as a more effective input for devices restoring locomotion in impaired people, such as robotic exoskeletons.Recent neuroscientific literature has shown that the use of brain-controlled robotic exoskeletons in walking rehabilitation induces neuroplasticity modi- fications, possibly leading to a higher likelihood of recovery and maintenance of lost motor functions due to a neural lesion, with respect to traditional re- habilitation. However, the gait decoding from brain signals remains an open challenge. The aim of this work is to implement and validate a deep learning model for online gait decoding that exploits Electroencephalography (EEG) infor- mation to predict the intention of initiating a step, which could be used to trigger the assistance of a lower-limb exoskeleton. In particular, the model exploits a Gated Recurrent Units (GRU) deep neural network to handle the time-dependent features which were identified by analysing the neural cor- relates preceding the step onset (i.e., Movement-Related Cortical Potentials (MRCP)). The network was evaluated on a pre-recorded dataset of 11 healthy subjects walking on a treadmill. The network’s architecture (e.g., number of GRU units) was optimized through grid search. In addition, to deal with the data scarcity problem of neurophysiological applications, I proposed a data augmentation procedure to increase the dataset available to train the model of each subject. With the proposed approach, the model achieved an average accuracy in detecting the step onset of 89.7 ± 7.7% with just the 15% of the dataset for each subject (∼70 steps), and up to 97.8 ± 1.3% with the whole dataset (∼440 steps). This thesis support the use of a memory-based deep learning model to de- code walking activity from non-invasive brain recordings. In future works, this model will be exploited in real time as a more effective input for devices restoring locomotion in impaired people, such as robotic exoskeletons

Corticomuscular co-activation based hybrid brain-computer interface for motor recovery monitoring

The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study established the feasibility of a new method of measuring corticomuscular co-activation called correlation of band-limited power time-courses (CBPT) of EEG and EMG signals, outperforming the traditional EEG-EMG coherence in terms of accurately controlling a robotic hand exoskeleton device by the stroke patients. In this paper, we have evaluated the neurophysiological significance of CBPT for motor recovery monitoring by conducting a 5-week long longitudinal pilot trial on 4 chronic hemiparetic stroke patients. Results show that the CBPT variations correlated significantly (p-value< 0.05) with the dynamic changes in motor outcome measures during the therapy for all the patients. As the bandpower based biomarkers are popular in literature, a comparison with such biomarkers has also been made to cross-verify whether the changes in CBPT are indeed neurophysiological. Thus the study concludes that CBPT can serve as a biomarker for motor recovery monitoring while serving as a corticomuscular co-activation feature for h-BCI based neurorehabilitation. Despite an observed significant positive change between pre- and post-intervention motor outcomes, the question of the clinical effectiveness of CBPT is subject to further controlled trial on a larger cohort