Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches

Impact of Generation Rate of Speech Imagery on Neural Activity and BCI Decoding Performance: A fNIRS Study

Brain-computer interface (BCI) enables stroke patients to actively modulate neural activity, fostering neuroplasticity and thereby accelerating the recovery process. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) has become one of the most widely used neuroimaging techniques. Current BCI research primarily focuses on improving the decoding performance. However, a key aspect of stroke rehabilitation lies in inducing stronger cortical activations in the damaged brain areas, thereby accelerating the recovery of brain functions. This study investigated the regulatory mechanism of the generation rate of speech imagery on neural activity and its impact on BCI decoding performance based on fNIRS. As the generation rate increased from 1 word/4 s to 1 word/2 s, and finally to 1 word/1 s, neural activity in speech-related brain regions steadily enhanced. Correspondingly, the accuracy of detecting speech imagery tasks increased from 83.83% to 85.39%, and ultimately showed a significant improvement, reaching 88.28%. Additionally, the differences in neural activities between the "yes" and "no" speech imagery tasks became more pronounced as the generation rate increased, leading to an improvement in classification performance from 62.81% to 65.78%, and ultimately to 67.50%. This study demonstrates that the neural activity level of most speech-related brain regions during speech imagery enhanced as the generation rate increased. Therefore, accelerating the generation rate of speech imagery induces stronger neural activity and more distinct response patterns between different tasks, which holds the potential to facilitate the development of a BCI feedback system with higher neuroplasticity induction and improved decoding performance.

The state-of-the-art of invasive brain-computer interfaces in humans: a systematic review and individual patient meta-analysis

Objective.Invasive brain-computer interfaces (iBCIs) have evolved significantly since the first neurotrophic electrode was implanted in a human subject three decades ago. Since then, both hardware and software advances have increased the iBCI performance to enable tasks such as decoding conversations in real-time and manipulating external limb prostheses with haptic feedback. In this systematic review, we aim to evaluate the advances in iBCI hardware, software and functionality and describe challenges and opportunities in the iBCI field.Approach.Medline, EMBASE, PubMed and Cochrane databases were searched from inception until 13 April 2024. Primary studies reporting the use of iBCI in human subjects to restore function were included. Endpoints extracted include iBCI electrode type, iBCI implantation, decoder algorithm, iBCI effector, testing and training methodology and functional outcomes. Narrative synthesis of outcomes was done with a focus on hardware and software development trends over time. Individual patient data (IPD) was also collected and an IPD meta-analysis was done to identify factors significant to iBCI performance.Main results.93 studies involving 214 patients were included in this systematic review. The median task performance accuracy for cursor control tasks was 76.00% (Interquartile range [IQR] = 21.2), for motor tasks was 80.00% (IQR = 23.3), and for communication tasks was 93.27% (IQR = 15.3). Current advances in iBCI software include use of recurrent neural network architectures as decoders, while hardware advances such as intravascular stentrodes provide a less invasive alternative for neural recording. Challenges include the lack of standardized testing paradigms for specific functional outcomes and issues with portability and chronicity limiting iBCI usage to laboratory settings.Significance.Our systematic review demonstrated the exponential rate at which iBCIs have evolved over the past two decades. Yet, more work is needed for widespread clinical adoption and translation to long-term home-use.

EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.

Static and Dynamic Functional Network Connectivity in Parkinson's Disease Patients With Postural Instability and Gait Disorder

AIMS

The exact cause of the parkinsonism gait remains uncertain. We first focus on understanding the underlying neurological reasons for these symptoms through the examination of both static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC).

METHODS

We recruited 64 postural instability and gait disorder-dominated Parkinson's disease (PIGD-PD) patients, 31 non-PIGD-PD (nPIGD-PD) patients, and 54 healthy controls (HC) from Nanjing Brain Hospital. The GIFT software identified five distinct independent components: the basal ganglia (BG), cerebellum (CB), sensory networks (SMN), default mode network (DMN), and central executive network (CEN). We conducted a comparison between the SFNC and DFNC of the five networks and analyzed their correlations with postural instability and gait disorder (PIGD) symptoms.

RESULTS

Compared with nPIGD-PD patients, the PIGD-PD patients demonstrated reduced connectivity between CEN and DMN while spending less mean dwell time (MDT) in state 4. This is characterized by strong connections. Compared with HC, PIGD-PD patients exhibited enhanced connectivity in the SFNC between CB and CEN, as well as the network between CB and DMN. Patients with PIGD-PD spent more MDT in state 1, which is characterized by few connections, and less MDT in state 4. In state 3, there was an increase in the functional connectivity between the CB and DMN in patients with PIGD-PD. The nPIGD patients showed increased SFNC connectivity between CB and DMN compared to HC. These patients spent more MDT in state 1 and less in state 4. The MDT and fractional windows of state 2 showed a positive link with PIGD scores.

CONCLUSION

Patients with PIGD-PD exhibit a higher likelihood of experiencing reduced brain connectivity and impaired information processing. The enhanced connection between the cerebellum and DMN networks is considered a type of dynamic compensation.

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

[Possibilities of mirror therapy in cognitive rehabilitation after stroke]

The review provides a brief overview of the history of the development of mirror therapy. Current data on the putative mechanisms of mirror therapy as well as the theory of mirror neurons are presented. The authors describe the implementation of the effects of mirror therapy in motor rehabilitation after stroke, including motor imagination or mental simulation of actions, strengthening of spatial attention and self-perception, activation of the ipsilateral corticospinal tract, reorganization of neuronal networks that influence the state of structurally intact but functionally inactive neurons. The authors reflected the prerequisites for the use of mirror therapy in the rehabilitation of cognitive impairment in poststroke patients. The results of current clinical studies and case reports of the use of mirror therapy for the rehabilitation of speech and non-speech cognitive disorders, and neglect syndrome after stroke are presented.