Brain-computer Interface Technology Research Articles

NeuroDM: Decoding and visualizing human brain activity with EEG-guided diffusion model

Background and Objective:Brain–Computer Interface (BCI) technology has recently been advancing rapidly, bringing significant hope for improving human health and quality of life. Decoding and visualizing visually evoked electroencephalography (EEG) signals into corresponding images plays a crucial role in the practical application of BCI technology. The recent emergence of diffusion models provides a good modeling basis for this work. However, the existing diffusion models still have great challenges in generating high-quality images from EEG, due to the low signal-to-noise ratio and strong randomness of EEG signals. The purpose of this study is to address the above-mentioned challenges by proposing a framework named NeuroDM that can decode human brain responses to visual stimuli from EEG-recorded brain activity. Methods:In NeuroDM, an EEG-Visual-Transformer (EV-Transformer) is used to extract the visual-related features with high classification accuracy from EEG signals, then an EEG-Guided Diffusion Model (EG-DM) is employed to synthesize high-quality images from the EEG visual-related features. Results:We conducted experiments on two EEG datasets (one is a forty-class dataset, and the other is a four-class dataset). In the task of EEG decoding, we achieved average accuracies of 99.80% and 92.07% on two datasets, respectively. In the task of EEG visualization, the Inception Score of the images generated by NeuroDM reached 15.04 and 8.67, respectively. All the above results outperform existing methods. Conclusions:The experimental results on two EEG datasets demonstrate the effectiveness of the NeuroDM framework, achieving state-of-the-art performance in terms of classification accuracy and image quality. Furthermore, our NeuroDM exhibits strong generalization capabilities and the ability to generate diverse images.

Brain-to-brain interface technology: A brief history, current state, and future goals.

A brain-to-brain interface (BBI), defined as a combination of neuroimaging and neurostimulation methods to extract and deliver information between brains directly without the need for the peripheral nervous system, is a budding communication technique. A BBI system is made up of two parts known as the brain-computer interface part, which reads a sender's brain activity and digitalizes it, and the computer-brain interface part, which writes the delivered brain activity to a receiving brain. As with other technologies, BBI systems have gone through an evolutionary process since they first appeared. The BBI systems have been employed for numerous purposes, including rehabilitation for post-stroke patients, communicating with patients suffering from amyotrophic lateral sclerosis, locked-in syndrome and speech problems following stroke. Also, it has been proposed that a BBI system could play an important role on future battlefields. This technology was not only employed for communicating between two human brains but also for making a direct communication path among different species through which motor or sensory commands could be sent and received. However, the application of BBI systems has provoked significant challenges to human rights principles due to their ability to access and manipulate human brain information. In this study, we aimed to review the brain-computer interface and computer-brain interface technologies as components of BBI systems, the development of BBI systems, applications of this technology, arising ethical issues and expectations for future use.

Breakthrough in Brain-Computer Interface Technology Paves the Way for Mind-Controlled Devices (BCI)

Breakthrough in Brain-Computer Interface Technology Paves the Way for Mind-Controlled Devices (BCI)

Frequency-dependent dynamics of steady-state visual evoked potentials under sustained flicker stimulation

Steady-state visual evoked potentials (SSVEP) are electroencephalographic signals elicited when the brain is exposed to a visual stimulus with a steady frequency. We analyzed the temporal dynamics of SSVEP during sustained flicker stimulation at 5, 10, 15, 20 and 40 Hz. We found that the amplitudes of the responses were not stable over time. For a 5 Hz stimulus, the responses progressively increased, while, for higher flicker frequencies, the amplitude increased during the first few seconds and often showed a continuous decline afterward. We hypothesize that these two distinct sets of frequency-dependent SSVEP signal properties reflect the contribution of parvocellular and magnocellular visual pathways generating sustained and transient responses, respectively. These results may have important applications for SSVEP signals used in research and brain-computer interface technology and may contribute to a better understanding of the frequency-dependent temporal mechanisms involved in the processing of prolonged periodic visual stimuli.

A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.

Ensemble classifiers fed by functional connectivity during cognitive processing differentiate Parkinson’s disease even being under medication

Brain–computer interface technologies, as a type of human-computer interaction, provide a control ability on machines and intelligent systems via human brain functions without needing physical contact. Moreover, it has a considerable contribution to the detection of cognitive state changes, which gives a clue for neurodegenerative diseases, including Parkinson’s disease (PD), in recent years. Although various studies implemented different machine learning models with several EEG features to detect PD and receive remarkable performances, there is a lack of knowledge on how brain connectivity during a cognitive task contributes to the differentiation of PD, even being under medication. To fill this gap, this study used three ensemble classifiers, which were fed by functional connectivity through cognitive response coherence (CRC) with varying selected features in different frequency bands upon application of the 3-Stimulation auditory oddball paradigm to differentiate PD medication ON and OFF and healthy controls (HC). The results revealed that the most remarkable performances were exhibited in slow frequency bands (delta and theta) in comparison to high frequency and wide range bands, especially in terms of target sounds. Moreover, in the delta band, target CRC distinguishes all groups from each other with accuracy rates of 80% for HC vs PD-OFF, 80% for HC vs PD-ON, and 81% for PD-ON vs PD-OFF. In the theta band, again target sounds were the most distinctive stimuli to classify HCxPD-OFF (80% accuracy), HCxPD-ON (80.5% accuracy) with quite good performances, and PD-ONxPD-OFF (76% accuracy) with acceptable performance. Besides, this study achieved a state-of-the-art performance with an accuracy of 87.5% in classifying PD-ONxPD-OFF via CRC of standard sounds in the delta band. Overall, the findings revealed that brain connectivity contributes to identifying PD and HC as well as the medication state of PD, especially in the slow frequency bands.

P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network.

Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.

Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification

BackgroundIn recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges. New methodThis paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise. ResultsThe suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively. Comparison with existing methodsIn terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods. ConclusionThe results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.

Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces.

Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.

Forecasting Epileptic Seizures Using XGBoost Methodology and EEG Signals

INTRODUCTION: Epilepsy denotes a disorder of neurological origin marked by repetitive and spontaneous seizures without any apparent trigger. Seizures occur due to abrupt and heightened electricity flowing through the brain, which can lead to physical and mental symptoms. There are several types of epileptic seizures, and epilepsy itself can be caused by various underlying conditions. EEG (Electroencephalogram) is one of the most important and widely used tools for epileptic seizure prediction and diagnosis. EEG uses skull sensors to record electrical signals from the brain., and it can provide valuable insights into brain activity patterns associated with seizures.
 OBJECTIVES: Brain-computer interface technology pathway for analyzing the EEG signals for seizure prediction to eliminate the class imbalance issue from our dataset in this case, a SMOTE approach is applied. It is observable that there are more classes of one variable than there are of the others in the output variable. This will be problematic when employing different Artificial intelligence techniques since these algorithms are more likely to be biased towards a certain variable because of its high prevalence
 METHODS: SMOTE approaches will be used to address this bias and balance the number of variables in the response variable. To develop an XGBoost (Extreme Gradient Boosting) model using SMOTE techniques to increase classification accuracy.
 RESULTS: The results show that the XGBoost method achieves a 98.7% accuracy rate.
 CONCLUSION: EEG-based model for seizure type using the XGBoost model for predicting the disease early. The Suggested method could significantly reduce the amount of time needed to accomplish seizure prediction.

The lived experiences of play and the perspectives of disabled children and their parents surrounding brain-computer interfaces

Brain-computer interfaces (BCI) offer promise to the play of children with significant physical impairments, as BCI technology can enable disabled children to control computer devices, toys, and robots using only their brain signals. However, there is little research on the unique needs of disabled children when it comes to BCI-enabled play. Thus, this paper explored the lived experiences of play for children with significant physical impairments and examined how BCI could potentially be implemented into disabled children’s play experiences by applying a social model of childhood disability. Descriptive qualitative methodology was employed by conducting four semi-structured interviews with two children with significant physical impairments and their parents. We found that disabled children’s play can be interpreted as passive or active depending on one’s definition and perceptions surrounding play. Moreover, disabled children continue to face physical, economic, and technological barriers in their play, as well as play restrictions from physical impairments. We urge that future research should strive to directly hear from disabled children themselves, as their perspectives may differ from their parents’ views. Also, future BCI development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments. Implications for Rehabilitation Assistive technology research should strive to examine the social, infrastructural, and environmental barriers that continue to disable and restrict participation for disabled children and their families through applying a social model of childhood disability and other holistic frameworks that look beyond individual factors Future research that examines the needs and lives of disabled children should strive to directly seek the opinions and perspectives of disabled children themselves Brain-computer interface development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments

Biomedical Engineering Research Based on Computer Propagation Technology: Treatment of neurodigestive diseases

With the support of the Institute of Cancer Hospital of Chifeng University, Sarkov and Markov Memorial Health Informatics Laboratory designed a new working concept of innovative Nursing by using neural network and Brain–computer interface technology. The research team proposed a direct connection between human or animal brain (or brain cell culture) and external equipment. In the case of one-way Brain–computer interface, the computer may accept commands from the brain. Using this point to detect the emotion of patients after surgery and carry out necessary gourds, a two-way information exchange method and model of two-way Brain–computer interface are also proposed. The potential non-invasive Brain–computer interface designed by us has been deeply studied, mainly because of its good time resolution, ease of use, portability and relatively low price. Especially in the specific application and working practice of EEG (Electroencephalography), our achievements directly meet the application of clinical institutions.

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.

The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals. This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task. The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL. For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal. The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human–robot collaboration

Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC. Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error. Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

Case Analysis on Three Methods of BCI Systems for Stroke Rehabilitation

Stroke is a disease that can cause disability to patients, and many people suffer from stroke. Brain-computer interface (BCI) technology development leads to BCI stroke rehabilitation system. After several years of development of theory and technology, BCI systems show great potential in stroke rehabilitation. There are three specific methods of BCI stroke rehabilitation systems. The methods involve Functional Electric Stimulation, Robotics Assisted Systems, and Virtual Reality. This paper will introduce three methods and analyze the research cases of each technique. The article will introduce the principle and investigate some research cases of each method. The paper will present the procedure, analyze the results and evaluate each experiment. By analyzing the cases, it can be learned that all three methods are feasible and effective, proving that the BCI system for stroke rehabilitation is a promising therapy. At last, limitations on BCI systems will be analyzed, such as small sample size and limitations on particular actions.

BCI Application in Stroke Rehabilitation: Robotic Assisted System

Stroke remains a pervasive global health concern, exacting enduring motor, cognitive, and emotional tolls on affected individuals. The evolving landscape of neural interfaces has recently shown promising strides in reinstating lost sensorimotor functions. Non-invasive brain-computer interfaces (BCIs) have gained widespread recognition for their inherent advantages—simplicity, safety, and cost-effectiveness. The ongoing refinement of BCIs involves addressing previous technological and neurophysiological limitations, focusing on understanding distinctive neurophysiological alterations observed in individuals with disabilities. The exploration of brain connectivity attributes is pivotal in implementing BCI-based control systems. Emerging as a notable prospect for post-stroke motor rehabilitation, Brain-Computer Interface (BCI)-based therapy has made significant progress in restoring sensorimotor function. Revolutionary advancements have been made to traditional brain-computer interface (BCI) systems, which rely on brain signals obtained from electroencephalography (EEG) and rule-based translation algorithms. This comprehensive review delves into the realm of BCI Robotic Assisted Systems, offering a nuanced analysis of contributions from diverse researchers. It meticulously dissects these systems' advantages and limitations, providing valuable insights into their efficacy. Ongoing endeavors within the field prioritize enhancing the portability, simplicity, and cost-effectiveness of BCI technology, ultimately refining its overall usability. As research progresses, emphasizing larger sample sizes, there is a tangible potential to augment the reliability of BCI systems for stroke rehabilitation significantly. This trajectory holds promise for extending the benefits of BCI technology to a broader spectrum of patients, marking a transformative leap in neurorehabilitation methodologiest.

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

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

Brain-Computer Interface: The Construction of Artificial Highways

One of the nifty advent of technology in terms of neurobionics is the Brain-Computer Interface (BCI). It will be no wrong to regard its principle somewhat likely to a real-time chip robot that deciphers human impulses, traversing on the nerve highways, into commands that modulate human actions which have been rendered paradoxical due to any physical or congenital nervous injuries. BCI technology has empowered the disabled, enabling them to operate cursors and smart home devices using their minds. The famous NeuraLink is emerging to be an advocate of the latest BCI technology, having performed a new brain implant successfully; but deep research puts forward the name of Synchron as an instigator of BCI industry. &#x0D; A tailed-electrode, with sixteen electric arrays, is pushed up-stream near to the brain’s motor cortex region via a small incision made in the jugular vein [1]. Upon reaching the destination it tubes out against the interior wall of the blood vessel, functioning as an antenna to detect the tissues’ electrical activity of the proximal cortex region such as moving a leg or scrolling through a screen. These signals are demodulated into muscle-translatable commands by a transmitter fitted in the chest, that disseminates the waves to an external digital device which performs the specified actions through a connected effector device. Primeval BCIs incorporated a mesh of protruding wires. Trials are underway to replace it with a home-based BCI setup. For now, fifty severely paralyzed people, including brain-stem stroke patients, have received these stents with a few Australians and Americans roaming around freely with implants in their blood vessels as part of BCI contemporary testing. &#x0D; Despite such bamboozling invention by Synchron, it has its limitations owing to the inferiority of brain signals espied across the curtains of membranes in way. A Synchron user can only scroll through the web pages. NeuraLink enters the competition by offering a 1024 electrodes’ matrix of ultrathin conducting bio-friendly fibers, integrated onto 1 centimeter’s cube film that can be inserted by a robot [1]. In January 2024 the first brain-film implant was performed on a brain-disabled patient which is showing signs of recovery. BCI current research revolves around inculcating the electrode bearing agent(s) directly into the brain regions without invasive procedures, which will be a breakthrough in neurosciences of the current era. &#x0D; Despite of its unprecedented utility, the BCI system also opens the gate for controversial scrutiny. For instance, there might be an undesired compulsion on BCI industry to escalate the amplitudes of cognition, memory, motor functions, and intellect even of normal individuals. Peculiarities like infrared vision and memory augmentation pose a danger towards informed consent and privacy. An environment of disparity, ascribable to the unequal approach of the interface, may be created that will lead to socio-political issues of genocide [2]. &#x0D; The anatomy of the human brain has this inherent tendency to reject any foreign agent playing with its natural traffic, thus for how long these electrodes can co-exist normally with human biology is a debate not yet started. In research a robot sensitive to empathy was generated and named ‘cobot’. With the use of an electroencephalographic (EEG) sensor worn by the human agent, its BCI was built. The cobot responds to the threat and passes it on to the recipient human agent, creating a virtuous cycle of caring and security. Consequently, when the patient was exposed to unsettling motion, an increase in the EEG signal was recorded [3]. The brain-computer interface fails to stamp out the placebo effect associated with the recovery. Is it really the chip or the unwavering willingness of the human mind to adapt and thrive ?

Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition.

In recent years, the integration of brain-computer interface technology and neural networks in the field of music generation has garnered widespread attention. These studies aimed to extract individual-specific emotional and state information from electroencephalogram (EEG) signals to generate unique musical compositions. While existing research has focused primarily on brain regions associated with emotions, this study extends this research to brain regions related to musical composition. To this end, a novel neural network model incorporating attention mechanisms and steady-state activation mapping (SSAM) was proposed. In this model, the self-attention module enhances task-related information in the current state matrix, while the extended attention module captures the importance of state matrices over different time frames. Additionally, a convolutional neural network layer is used to capture spatial information. Finally, the ECA module integrates the frequency information learned by the model in each of the four frequency bands, mapping these by learning their complementary frequency information into the final attention representation. Evaluations conducted on a dataset specifically constructed for this study revealed that the model surpassed representative models in the emotion recognition field, with recognition rate improvements of 1.47% and 3.83% for two different music states. Analysis of the attention matrix indicates that the left frontal lobe and occipital lobe are the most critical brain regions in distinguishing between 'recall and creation' states, while FP1, FPZ, O1, OZ, and O2 are the electrodes most related to this state. In our study of the correlations and significances between these areas and other electrodes, we found that individuals with musical training exhibit more extensive functional connectivity across multiple brain regions. This discovery not only deepens our understanding of how musical training can enhance the brain's ability to work in coordination but also provides crucial guidance for the advancement of brain-computer music generation technologies, particularly in the selection of key brain areas and electrode configurations. We hope our research can guide the work of EEG-based music generation to create better and more personalized music.

Design and optimization of a 16-bit spike detector based on 90nm CMOS technology

To deeply understanding of brain function, the rapid and accurate extracting relevant signals during brain operation has become increasingly important. As our understanding of brain function advances, the ability to extract relevant signals quickly and accurately becomes increasingly crucial. Therefore, a 16-bit spike detector has been designed to detect neural signals. Since a 4-bit absolute value detector is a critical module of the 16-bit spike detector, this paper presents a design for a 4-bit value comparator using a chain adder and a comparator. Two methods, changing the supply voltage and changing the gate size, were utilized to reduce power consumption, and it was found that changing the supply voltage method was more effective. Additionally, the circuits layout was designed for simulation to enhance the authenticity and reliability of the results. Currently, brain-computer interface technology remains a widely discussed topic in the field of neuroscience, and the speed and accuracy of neural signal processing continue to be critical issues that need to be addressed.