Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.

Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the "sixth-finger" (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm. Twenty stroke patients were recruited for this experiment. Event-related spectral perturbation was adopted to supply details about ERD. Brain activation region, intensity and functional connectivity were compared between SF-MI and AH-MI to reveal the ERD enhancement performance of novel MI paradigm. A "wider range, stronger intensity, greater connection" ERD activation pattern was induced in stroke patients by novel SF-MI paradigm compared to traditional AH-MI paradigm. The bilateral sensorimotor and prefrontal modulation was found in SF-MI, which was different in AH-MI only weak sensorimotor modulation was exhibited. The ERD enhancement is mainly concentrated in mu rhythm. More synchronized and intimate neural activity between different brain regions was found during SF-MI tasks compared to AH-MI tasks. Classification results (>80% in SF-MI vs. REST) also indicated the feasibility of applying novel MI paradigm to clinical stroke rehabilitation. This work provides a novel MI paradigm and demonstrates its neural activation-enhancing performance, helping to develop more effective MI-based BCI system for stroke rehabilitation.

Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.

Background: Fatigue after stroke is hard to define and measure and how it is associated with other complications after stroke still needs to be explored. These issues are relevant in stroke rehabilitation and in the patient’s daily life.Objective: To investigate fatigue after stroke and its relation to balance, gait, and Binocular Visual Dysfunction (BVD).Methods: Adults with stroke (n = 29, age 18–67 years) were tested with the Modified Fatigue Impact Scale (MFIS), objective and subjective BVD measures, Balance Evaluation Systems Test, Ten Meter Walk Test, and a Health-Related Quality of Life questionnaire, before and after a four-month intervention program and at three- and six-month follow-ups. We used principle component analysis to extract underlying factors of MFIS. Associations between MFIS factors and patient characteristics were analyzed by repeated measures ANOVA. The associations between MFIS factors and physical measures were assessed using pairwise correlations.Results: Three components were extracted from the MFIS, explaining 71% of variance: Cognitive fatigue, Physical fatigue and Arousal. We found that women register higher MFIS scores than men. There was a strong association between the level of Cognitive and Physical Fatigue and BVD, between Arousal and balance and dizziness, and between Cognitive Fatigue and gait.Conclusion: The three extracted components of MFIS proved clinically informative. The arousal component revealed particularly interesting results in studying fatigue. The correlation analysis shown at this component differs from cognitive and physical fatigue and describes another aspect of PSF, important in future treatment and research.

Many of the compounds in drugs cannot be effectively delivered using current drug delivery techniques (e.g., pills and injections). Transdermal delivery is an attractive alternative, but it is limited by the extremely low permeability of the skin. As the primary barrier to transport is located in the upper tissue, Micro-Electro-Mechanical-System (MEMS) technology provides novel means, such as microneedle array and PZT pump, in order to increase permeability of human skin with efficiency, safety and painless delivery, and to decrease the size of the pump. Microneedle array has many advantages, including minimal trauma at penetration site because of the small size of the needle, free from condition limitations, painless drug delivery, and precise control of penetration depth. These will promote the development of biomedical sciences and technology and make medical devices more humanized. So far, most of the insulin pumps being used are mechanical pumps. We present the first development of this novel technology, which can assemble the PZT pump and the microneedle array together for diabetes mellitus. The microneedle array based on a flexible substrate can be mounted on non-planar surface or even on flexible objects such as a human fingers and arms. The PZT pump can pump the much more precision drug accurately than mechanical pump and the overall size is much smaller than those mechanical pumps. The hollow wall straight microneedle array is fabricated on a flexible silicon substrate by inductively coupled plasma (ICP) and anisotropic wet etching techniques. The fabricated hollow microneedles are 200 μm in length and 30 μm in diameter. The microneedle array, which is built with on-board fluid pumps, has potential applications in the chemical and biomedical fields for localized chemical analysis, programmable drug-delivery systems, and very small, precise fluids sampling. The microneedle array has been installed in an insulin pump for demonstration and a leak free packaging is introduced.

Brain-Computer Interface (BCI) systems allow user to operate devices without muscular activation and experimental results indicate that they can induce activity-dependent plasticity. A BCI system has two key features: exploitation of brain signal changes induced by the cognitive task assigned to the user and mutual learning. With respect to the first feature, BCI users can learn to control and hence change their cortical activity, for example by performing a motor imagery task. Mutual learning, on the other hand, is set up between users, which have to learn how to control their brain activity, and the classifier used for learning and categorize brain signal changes. This mutual learning can be mediated by the use of feedback, which in turn can be discrete or continuous, abstract or realistic and so on. Motor imagery represents a promising approach for stroke rehabilitation, thus a BCI based on a motor imagery paradigm could be a useful tool. Nevertheless, in stroke rehabilitation settings two issues are emerging which are also crucial for motor imagery BCIs. The first one relates to the importance of recognize the imagery ability of the subject, the second one to the importance of feedback in re-learning of motor skills. The present study relates to this second issue and it is aimed at investigating attitudes of BCI users towards feedback. We considered a sample of six healthy males, using an EEG-based BCI and executing a Motor Imagery task without feedback (training phase) and with feedback (performance phase). A horizontal bar of varying length depicted on a computer screen was used as feedback medium and overall accuracy data were matched with neurophysiological data acquired from P z and O z . Results could be of importance for BCI use as neurorehabilitation tool in stroke rehabilitation protocols since they suggest that some subjects could need an increased cortical activation in order to deal with a stimulus like visual feedback. Moreover, they reveal a possible association between worsening in performance and increased cortical activation needed in order to cope with feedback.

Microneedle arrays are a simple, noninvasive transdermal delivery system. The technique's preparation, optimization, and scaling up are all active research topics. This paper investigates a simple method for making microneedle molds for inverted-solvent casting preparation of microneedle arrays. The effect of different resins and printing conditions on the geometry of microneedle arrays was investigated using stereolithography 3D printing. Four molds prepared in this study were selected to be filled with candesartan cilexetil in a mixture of polymers that include EMPROVE® and gantrez. The fully formed MNAs were evaluated based on skin perforation, drug release through Franz cells, bending ability, and sterilization. The model drug and the resultant MNAs were evaluated based on the MNA's shape, drug release, and compression resistance. To improve process quality and achieve highly aligned defect-free needles, the surface of the fabricated microneedles was modified with a monolayer of hyaluronic acid. The structure design was optimized using computer-aided design. The molds were tested by producing candesartan cilexetil microneedle array patches for transdermal delivery. This study discovered that hot plate post-treatment and avoiding shear force during mold peeling are critical for fabricating high-quality arrays. Candesartan cilexetil microneedles were successfully prepared, and the release rate was 73.36 ± 7.29% of the loaded candesartan cilexetil amount over 24 hours. The obtained molds can potentially be used to fabricate microneedle arrays successfully.

In this paper, we proposed a flexible wearable patch integrating parylene-based microneedle array (MNA) and signal acquisition flexible PCB (FPC) circuit for long-term biopotential monitoring. The MNA can penetrate high-resistance stratum corneum to achieve low-impedance skin-electrode contact. Due to the flexibility of Parylene and polyimide (PI) substrate, the device can contact with soft and curved body surface compactly. Furthermore, the button battery, signal acquisition chip and Bluetooth transceiver were integrated and minimized to a small patch, leading to increased wearability. The system is expected to form a sensing net on the whole body to achieve multichannel biopotential monitoring.

Large language models or ChatGPT have recently gained extensive media coverage. At the same time, the use of ChatGPT has increased deistically. Biomedical researchers, engineers, and clinicians have shown significant interest and started using it due to its diverse applications, especially in the biomedical field. However, it has been found that ChatGPT sometimes provided incorrect or partly correct information. Itis unable to give the most recent information. Therefore, we urgently advocate a domain-specific next-generation, ChatBot for biomedical engineering and research, providing error-free, more accurate, and updated information. The domain-specific ChatBot can perform diversified functions in biomedical engineering, such as performing innovation in biomedical engineering, designing a medical device, etc. The domain-specific artificial intelligence enabled device will revolutionize biomedical engineering and research if a biomedical domain-specific ChatBot is produced.

Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG

Vibration stimulation has been shown to have the potential to improve the activation pattern of unilateral motor imagery (MI) and to promote motor recovery. However, in the widely used left and right hand MI brain-computer interface (BCI) paradigm, the vibration stimuli cannot be directly applied to the imaginary side due to the spontaneity of imagery. In this study, we proposed a method of phase-dependent closed-loop vibration stimulation to be applied on both hands, and explored the effects of different vibration stimuli on the left and right hand MI-BCI. Eighteen healthy subjects were recruited and asked to perform, in sequence, MI tasks under three different conditions of vibratory feedback, which were no vibration stimulus (MI), phase-dependent closed-loop vibration stimulus (PDS), and continuous vibration stimulus (CS). Then the performance of the left and right hand MI-BCI and the patterns of brain oscillation were compared and analyzed under these different stimulation conditions. The results showed that vibration stimulation effectively boosted the activation of the sensorimotor cortex and enhanced the functional connectivity among sensorimotor-related brain regions during MI. The closed-loop stimulation evoked stronger event-related desynchronization patterns on the contralateral side of the imagined hand compared to continuous stimulation. There was a more obvious distinction between left hand task and right hand task. In addition, phase-dependent closed-loop vibration stimulation increased classification accuracy by approximately 7% (paired t-test, p=0.004, n=18) compared to MI alone, while continuous vibration stimulation only increased it by 4% (paired t-test, p=0.067, n=18). This result further demonstrated the effectiveness of the phase-dependent closed-loop vibration stimulation method in improving the overall performance of the MI paradigm and is expected to be further applied in areas such as stroke rehabilitation in the future.

This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work. Features Presents an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods Covers various theoretical considerations of adaptive filtering techniques used for DNA analysis, introduction to adaptive filtering, properties of adaptive algorithms, need for development of adaptive exon predictors (AEPs), and structure of AEP used for DNA analysis Extends the approach of LMS algorithm and its sign-based realizations with normalization factor for DNA analysis Presents the normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants Provides an overview of the goals achieved and highlights the primary achievements using all proposed techniques Dr. Md. Zia Ur Rahman is a professor in the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, genetic engineering, medical imaging, array signal processing, medical telemetry, and nanophotonics. Dr. Srinivasareddy Putluri is currently a Software Engineer at Tata Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree (Genomic Signal Processing using Adaptive Signal Processing algorithms) from the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His research interests include genomic signal processing and adaptive signal processing. He has published 15 research papers in various journals and proceedings. He is currently a reviewer of publishers like the IEEE Access and IGI.

Analysis of tissue and arterial blood temperatures in the resting human forearm. 1948.

Cognitive fatigue is the natural result of longtime mental effort during the execution of a high mental workload or a strenuous task. This situation often leads to decreased productivity and increased security risks. In this study, it was aimed to detect cognitive fatigue quickly and accurately, regardless of subjective data. CogBeacon dataset was used for this. Data that make up the CogBeacon dataset were collected from 19 participants in 76 sessions with the help of a 4-electrode MUSE electroencephalography (EEG) device. The collected raw EEGs were randomly separated and feature extraction was performed. Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) algorithms were used in the classification process. Katz and Higuchi Fractal Dimension, standard deviation, median, variance and covariance were tested as features. When the classification was made with SVM, the education average was 93.99% and the test average was 83.14%. The average success rate increased between 4.43% and 7.40%, compared to the trials that were not used in the trials where Fractal Dimension features were used. When the classification was made with KNN, the education averange was 91.71% and the test average was 83.34%. The average success rate increased between 5.10% and 8.92% compared to the trials that were not used in the trials in which Fractal Dimension features were used.

Purpose Cognitive fatigue is commonly reported and described as disabling by patients recovering from neurological conditions including stroke. However, cognitive fatigue is usually underdiagnosed among stroke survivors which leads to a lack of specific treatments for this condition. Therefore, the aim of this study was to explore post-stroke cognitive fatigue as it is experienced by stroke survivors. Methods This qualitative research followed the principles of descriptive phenomenology within a constructivist paradigm. Individual semi-structured interviews were conducted with stroke survivors experiencing post-stroke cognitive fatigue recruited through the Heart and Stroke Foundation, the Canadian Partnership for Stroke Recovery, and social media posts. Data were analyzed through inductive content analysis. Results Eleven stroke survivors participated. The analysis revealed five themes illustrating the experience and descriptions of post-stroke cognitive fatigue: (1) characteristics, (2) aggravating factors, (3) management, (4) effect of cognitive fatigue on daily life, and (5) social awareness and support. Conclusion This study highlights the complexity of post-stroke cognitive fatigue. Cognitive fatigue becomes more evident after discharge; therefore, clinicians should consistently screen for it and provide proper education to the patients and their carers. IMPLICATIONS FOR REHABILITATION Cognitive fatigue is a complex phenomenon that can negatively affect the daily life of stroke survivors. Sensory-overloaded environments, emotional distress, poor sleep, and engaging in complex cognitive tasks can trigger post-stroke cognitive fatigue. More education on the concept of cognitive fatigue should be provided to healthcare providers to be able to identify and manage this symptom properly.

Flexible electronic sensors, due to their mechanical compliance with soft tissues and excellent biocompatibility, have demonstrated unique advantages in neuroscience research and clinical applications in recent years. Compared with traditional rigid electrodes, flexible sensors show greater potential in long-term stability, large-area coverage, and multimodal integration, thereby meeting the diverse needs of neural signal acquisition and functional monitoring. This review summarizes the latest advances in the application of flexible electronics for brain monitoring, covering electrophysiological signal acquisition, neurotransmitter detection, multimodal and region-synchronized recording, and clinical rehabilitation applications. It further discusses critical aspects influencing performance and applications, including material selection, device design, circuit integration, energy supply, and mechanical modeling. On this basis, the challenges and prospects regarding long-term stability, data processing, multimodal integration, and clinical translation are analyzed. Overall, flexible brain sensors are gradually progressing from laboratory validation to systematic applications, and their development is expected to exert profound impacts on neuroscience research, disease diagnosis and treatment, and intelligent rehabilitation in the future.