Brain-Computer Interface-Based Humanoid Control: A Review

On the role of generative artificial intelligence in the development of brain-computer interfaces

Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held promise to compensate for functions lost by people with disabilities through allowing direct communication between the brain and external devices. While research throughout the past decades has demonstrated the feasibility of BCI to act as a successful assistive technology, the widespread use of BCI outside the lab is still beyond reach. This can be attributed to a number of challenges that need to be addressed for BCI to be of practical use including limited data availability, limited temporal and spatial resolutions of brain signals recorded non-invasively and inter-subject variability. In addition, for a very long time, BCI development has been mainly confined to specific simple brain patterns, while developing other BCI applications relying on complex brain patterns has been proven infeasible. Generative Artificial Intelligence (GAI) has recently emerged as an artificial intelligence domain in which trained models can be used to generate new data with properties resembling that of available data. Given the enhancements observed in other domains that possess similar challenges to BCI development, GAI has been recently employed in a multitude of BCI development applications to generate synthetic brain activity; thereby, augmenting the recorded brain activity. Here, a brief review of the recent adoption of GAI techniques to overcome the aforementioned BCI challenges is provided demonstrating the enhancements achieved using GAI techniques in augmenting limited EEG data, enhancing the spatiotemporal resolution of recorded EEG data, enhancing cross-subject performance of BCI systems and implementing end-to-end BCI applications. GAI could represent the means by which BCI would be transformed into a prevalent assistive technology, thereby improving the quality of life of people with disabilities, and helping in adopting BCI as an emerging human-computer interaction technology for general use.

An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification

Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-based BCI has largely contributed to medical fields and smart home industry. However, because of the low signal-to-noise ratio (SNR) and the non-stationary characteristic of EEG data, it is difficult to correctly classify different types of MI-EEG signals. Recently, the advances in Deep Learning (DL) significantly facilitate the development of MI EEG-based BCIs. In this paper, we provide a systematic survey of DL-based MI-EEG classification methods. Specifically, we first comprehensively discuss several important aspects of DL-based MI-EEG classification, covering input formulations, network architectures, public datasets, etc. Then, we summarize problems in model performance comparison and give guidelines to future studies for fair performance comparison. Next, we fairly evaluate the representative DL-based models using source code released by the authors and meticulously analyse the evaluation results. By performing ablation study on the network architecture, we found that (1) effective feature fusion is indispensable for multi-stream CNN-based models. (2) LSTM should be combined with spatial feature extraction techniques to obtain good classification performance. (3) the use of dropout contributes little to improving the model performance, and that (4) adding fully connected layers to the models significantly increases their parameters but it might not improve their performance. Finally, we raise several open issues in MI-EEG classification and provide possible future research directions.

Efficacy of brain-computer interfaces on upper extremity motor function rehabilitation after stroke: A systematic review and meta-analysis

BACKGROUND

The recovery of upper limb function is crucial to the daily life activities of stroke patients. Brain-computer interface technology may have potential benefits in treating upper limb dysfunction.

OBJECTIVE

To systematically evaluate the efficacy of brain-computer interfaces (BCI) in the rehabilitation of upper limb motor function in stroke patients.

METHODS

Six databases up to July 2023 were reviewed according to the PRSIMA guidelines. Randomized controlled trials of BCI-based upper limb functional rehabilitation for stroke patients were selected for meta-analysis by pooling standardized mean difference (SMD) to summarize the evidence. The Cochrane risk of bias tool was used to assess the methodological quality of the included studies.

RESULTS

Twenty-five studies were included. The studies showed that BCI had a small effect on the improvement of upper limb function after the intervention. In terms of total duration of training, < 12 hours of training may result in better rehabilitation, but training duration greater than 12 hours suggests a non significant therapeutic effect of BCI training.

CONCLUSION

This meta-analysis suggests that BCI has a slight efficacy in improving upper limb function and has favorable long-term outcomes. In terms of total duration of training, < 12 hours of training may lead to better rehabilitation.

Impact of new technologies on neurology in Spain. Review by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology

INTRODUCTION

New technologies are increasingly widespread in biomedicine. Using the consensus definition of new technologies established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN.

MATERIAL AND METHODS

We defined the concept of new technology in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of technology, the field of neurology, and the geographical provenance of the studies.

RESULTS

We identified 299 communications related with new technologies from a total of 8139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where new technologies were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to new technologies (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%).

CONCLUSIONS

The number of communications addressing new technologies follows an upward trend. The number of technologies used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.

Effects of Motor Imagery Training for Lower Limb Dysfunction in Patients With Stroke: A Systematic Review and Meta-analysis of Randomized Controlled Trials

OBJECTIVE

The aim of the study is to determine the effects of motor imagery training associated with conventional rehabilitation therapies on lower limb motor function recovery in poststroke patients.

DESIGN

Comprehensive literature searches were performed to identify studies published before June 5, 2022. RevMan 5.3 software was used for meta-analysis. The quality of the included studies was assessed using the Cochrane risk-of-bias tool and the modified Jadad scale. The certainty of the evidence was evaluated with the Grading of Recommendations, Assessment, Development and Evaluations system.

RESULT

Twenty-three trials and/or 1109 participants with motor imagery training ability were included in this review. Motor imagery training combined with conventional rehabilitation therapies versus conventional rehabilitation therapies demonstrated significant benefits in motor function, balance function, temporospatial gait variables (walking speed, stride length, and cadence) and activities of daily living. Subgroup analysis demonstrated that motor imagery training-conventional rehabilitation therapies had a better effect on improvement in motor function and activities of daily living in the acute phase and had a greater benefit on walking speed in the chronic phase. In addition, motor imagery training-conventional rehabilitation therapies resulted in greater improvements in stride length, cadence, walking ability, and balance function in the subacute phase.

CONCLUSIONS

Motor imagery training-conventional rehabilitation therapies have better effects on the recovery of lower limb motor function in poststroke patients than conventional rehabilitation therapies, which may be most beneficial for lower extremity motor function recovery in the first 7 days to 6 mos.

Machine learning in biosignals processing for mental health: A narrative review

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.

Identifying Thematics in a Brain-Computer Interface Research

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.

Locked-in syndrome revisited

The locked-in syndrome (LiS) is characterized by quadriplegia with preserved vertical eye and eyelid movements and retained cognitive abilities. Subcategorization, aetiologies and the anatomical foundation of LiS are discussed. The damage of different structures in the pons, mesencephalon and thalamus are attributed to symptoms of classical, complete and incomplete LiS and the locked-in plus syndrome, which is characterized by additional impairments of consciousness, making the clinical distinction to other chronic disorders of consciousness at times difficult. Other differential diagnoses are cognitive motor dissociation (CMD) and akinetic mutism. Treatment options are reviewed and an early, interdisciplinary and aggressive approach, including the provision of psychological support and coping strategies is favoured. The establishment of communication is a main goal of rehabilitation. Finally, the quality of life of LiS patients and ethical implications are considered. While patients with LiS report a high quality of life and well-being, medical professionals and caregivers have largely pessimistic perceptions. The negative view on life with LiS must be overthought and the autonomy and dignity of LiS patients prioritized. Knowledge has to be disseminated, diagnostics accelerated and technical support system development promoted. More well-designed research but also more awareness of the needs of LiS patients and their perception as individual persons is needed to enable a life with LiS that is worth living.

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials

Background

Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices.

Methods

English-language literature published before April 1, 2022, was searched in five electronic databases using search terms including “brain-computer/machine interface”, “stroke” and “upper extremity.” The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.4.1 software. The GRADE method was used to assess the quality of the evidence.

Results

A total of 17 studies with 410 post-stroke patients were included. Meta-analysis showed that BMI-based training significantly improved upper extremity motor function [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I² = 38%; p < 0.0001; n = 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated that BMI-based training significantly improves upper extremity motor function in both chronic [SMD = 0.68; 95% CI (0.32, 1.03), I² = 46%; p = 0.0002, random-effects model] and subacute [SMD = 1.11; 95%CI (0.22, 1.99); I² = 76%; p = 0.01; random-effects model] stroke patients compared with control interventions, and using functional electrical stimulation (FES) [SMD = 1.11; 95% CI (0.67, 1.54); I² = 11%; p < 0.00001; random-effects model]or visual feedback [SMD = 0.66; 95% CI (0.2, 1.12); I² = 4%; p = 0.005; random-effects model;] as the feedback devices in BMI training was more effective than using robot. In addition, BMI-based training was more effective in improving patients' activities of daily living (ADL) than control interventions [SMD = 1.12; 95% CI (0.65, 1.60); I² = 0%; p < 0.00001; n = 80; random-effects model]. There was no statistical difference in the dropout rate and adverse effects between the BMI-based training group and the control group.

Conclusion

BMI-based training improved upper limb motor function and ADL in post-stroke patients. BMI combined with FES or visual feedback may be a better combination for functional recovery than robot. BMI-based trainings are well-tolerated and associated with mild adverse effects.

Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture

Recently, motor imagery EEG signals have been widely applied in Brain–Computer Interfaces (BCI). These signals are typically observed in the first motor cortex of the brain, resulting from the imagination of body limb movements. For non-invasive BCI systems, it is not apparent how to locate the electrodes, optimizing the accuracy for a given task. This study proposes a comparative analysis of channel signals exploiting the Deep Learning (DL) technique and a public dataset to locate the most discriminant channels. EEG channels are usually selected based on the function and nomenclature of electrode location from international standards. Instead, the most suitable configuration for a given paradigm must be determined by analyzing the proper selection of the channels. Therefore, an EEGNet network was implemented to classify signals from different channel location using the accuracy metric. Achieved results were then contrasted with results from the state-of-the-art. As a result, the proposed method improved BCI classification accuracy.

Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level

Several methods have been used to quantify human movement at different levels, from coordinated multi joint movements to those taking place at the single muscle level. These methods are developed either in order to allow us to interact with computers and machines, or to use such technologies for aiding rehabilitation among those with mobility impairments or movement disorders. Human machine interfaces typically rely on some existing human movement ability and measure it using motion tracking or inertial measurement units, while the rehabilitation applications may require us to measure human motor intent. Surface or implanted electrodes, electromyography, electroencephalography, and brain computer interfaces are beneficial in this regard, but have their own shortcomings. We have previously shown feasibility of using ultrasound imaging (Sonomyography) to infer human motor intent and allow users to control external biomechatronic devices such as prosthetics. Here, we asked users to freely move their hand in three different movement patterns, measuring their actual joint angles and passively computing their Sonomyographic output signal. We found a high correlation between these two signals, demonstrating that the Sonomyography signal is not only user-controlled and stable, but it is closely linked with the user's actual movement level. These results could help design wearable rehabilitation or human computer interaction devices based on Sonomyography to decode human motor intent.

Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory

Controller design and signal processing for the control of air-vehicles have gained extreme importance while interacting with humans to form a brain–computer interface. This is because fewer commands need to be mapped into multiple controls. For our anticipated biomedical sensor for breath analysis, it is mandatory to provide medication to the patients on an urgent basis. To address this increasingly tense situation in terms of emergencies, we plan to design an unmanned vehicle that can aid spontaneously to monitor the person’s health, and help the physician spontaneously during the rescue mission. Simultaneously, that must be done in such a computationally efficient algorithm that the minimum amount of energy resources are consumed. For this purpose, we resort to an unmanned logistic air-vehicle which flies from the medical centre to the affected person. After obtaining restricted permission from the regional administration, numerous challenges are identified for this design. The device is able to lift a weight of 2 kg successfully which is required for most emergency medications, while choosing the smallest distance to the destination with the GPS. By recording the movement of the vehicle in numerous directions, the results deviate to a maximum of 2% from theoretical investigations. In this way, our biomedical sensor provides critical information to the physician, who is able to provide medication to the patient urgently. On account of reasonable supply of medicines to the destination in terms of weight and time, this experimentation has been rendered satisfactory by the relevant physicians in the vicinity.

Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps

An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

A Human-Machine Interface Based on an EOG and a Gyroscope for Humanoid Robot Control and Its Application to Home Services

The human-machine interface (HMI) has been studied for robot teleoperation with the aim of empowering people who experience motor disabilities to increase their interaction with the physical environment. The challenge of an HMI for robot control is to rapidly, accurately, and sufficiently produce control commands. In this paper, an asynchronous HMI based on an electrooculogram (EOG) and a gyroscope is proposed using two self-paced and endogenous features, double blink and head rotation. By designing the multilevel graphical user interface (GUI), the user can rotate his head to move the cursor of the GUI and create a double blink to trigger the button in the interface. The proposed HMI is able to supply sufficient commands at the same time with high accuracy (ACC) and low response time (RT). In the trigger task of sixteen healthy subjects, the target was clicked from 20 options with ACC of 99.2% and RT 2.34 s. Furthermore, a continuous strategy that uses motion start and motion stop commands to create a certain robot motion is proposed to control a humanoid robot based on the HMI. It avoids the situation that combines some commands to achieve one motion or converts the certain motion to a command directly. In the home service experiment, all subjects operated a humanoid robot changing the state of a switch, grasping a key, and putting it into a box. The time ratio between HMI control and manual control was 1.22, and the number of commands ratio was 1.18. The results demonstrated that the continuous strategy and proposed HMI can improve performance in humanoid robot control.

Multi-scale Chimerism: An experimental window on the algorithms of anatomical control

Despite the immense progress in genetics and cell biology, major knowledge gaps remain with respect to prediction and control of the global morphologies that will result from the cooperation of cells with known genomes. The understanding of cooperativity, competition, and synergy across diverse biological scales has been obscured by a focus on standard model systems that exhibit invariant species-specific anatomies. Morphogenesis of chimeric biological material is an especially instructive window on the control of biological growth and form because it emphasizes the need for prediction without reliance on familiar, standard outcomes. Here, we review an important and fascinating body of data from experiments utilizing DNA transfer, cell transplantation, organ grafting, and parabiosis. We suggest that these are all instances (at different levels of organization) of one general phenomenon: chimerism. Multi-scale chimeras are a powerful conceptual and experimental tool with which to probe the mapping between properties of components and large-scale anatomy: the laws of morphogenesis. The existing data and future advances in this field will impact not only the understanding of cooperation and the evolution of body forms, but also the design of strategies for system-level outcomes in regenerative medicine and swarm robotics.

Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

Does Creativity Influence Visual Perception? - An Event-Related Potential Study With Younger and Older Adults

We do not know enough about the cognitive background of creativity despite its significance. Using an active oddball paradigm with unambiguous and ambiguous portrait paintings as the standard stimuli, our aim was to examine whether: creativity in the figural domain influences the perception of visual stimuli; any stages of visual processing; or if healthy aging has an effect on these processes. We investigated event related potentials (ERPs) and applied ERP decoding analyses in four groups: younger less creative; younger creative; older less creative; and older creative adults. The early visual processing did not differ between creativity groups. In the later ERP stages the amplitude for the creative compared with the less creative groups was larger between 300 and 500 ms. The stimuli types were clearly distinguishable: within the 300–500 ms range the amplitude was larger for ambiguous rather than unambiguous paintings, but this difference in the traditional ERP analysis was only observable in the younger, not elderly groups, who also had this difference when using decoding analysis. Our results could not prove that visual creativity influences the early stage of perception, but showed creativity had an effect on stimulus processing in the 300–500 ms range, in indexing differences in top-down control, and having more flexible cognitive control in the younger creative group.

Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity

Decoding brain intention from noninvasively measured neural signals has recently been a hot topic in brain-computer interface (BCI). The motor commands about the movements of fine parts can increase the degrees of freedom under control and be applied to external equipment without stimulus. In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on functional connectivity was proposed to decode the motor intention of four fine parts movements (shoulder, elbow, wrist, hand). First, event-related desynchronization was analyzed to reveal the differences between the four classes. Second, functional connectivity was constructed by using synchronization likelihood (SL), phase-locking value (PLV), H index (H), mutual information (MI), and weighted phase-lag index (WPLI) to acquire the electrode pairs with a difference. Subsequently, a GCN and convolutional neural networks (CNN) were performed based on functional topological structures and time points, respectively. The results demonstrated that the proposed method achieved a decoding accuracy of up to 92.81% in the four-class task. Besides, the combination of GCN and functional connectivity can promote the development of BCI.

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces

Brain-computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human-machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.

Development and Progress in Sensors and Technologies for Human Emotion Recognition

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

A Hybrid Brain-Computer Interface for Real-Life Meal-Assist Robot Control

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain-computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user's intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.

Detecting Attention Levels in ADHD Children with a Video Game and the Measurement of Brain Activity with a Single-Channel BCI Headset

Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to train attention and to provide a measure to identify attentional problems in children early on. Attention changes registered with NeuroSky MindWave in combination with the CARAS-R psychological test were used to characterise the attentional profiles of 52 non-ADHD and 23 ADHD children aged 7 to 12 years old. The analyses revealed that the GokEvolution was valuable in measuring attention through its use of EEG–BCI technology. The ADHD group showed lower levels of attention and more variability in brain attentional responses when compared to the control group. The application was able to map the low attention profiles of the ADHD group when compared to the control group and could distinguish between participants who completed the task and those who did not. Therefore, this system could potentially be used in clinical settings as a screening tool for early detection of attentional traits in order to prevent their development.

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.

Impact of new technologies on neurology in Spain. Review by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology

INTRODUCTION

New technologies (NT) are increasingly widespread in biomedicine. Using the consensus definition of NT established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN.

MATERIAL AND METHODS

We defined the concept of NT in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of NT, the field of neurology, and the geographical provenance of the studies.

RESULTS

We identified 299 communications related with NT from a total of 8,139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where NT were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to NT (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%).

CONCLUSIONS

The number of communications addressing NT follows an upward trend. The number of NT used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.