Human Brain Signals Research Articles

Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.

Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.

Emotion Detection using EEG Signals in Image and Video Processing: A Survey

Brain serves as the body's processing of knowledge and management centre. The central nervous system directly produces ElectroEncephaloGram (EEG) physiological signals, which are strongly associated with human emotions. In the upcoming years, there will be an increase in interest for identifying emotion using brain waves by EEG (ElectroEncephaloGraphy) signals. It takes efficient and effective signal processing and feature extraction techniques for detection of emotions from human biological brain signals. Current approaches gather valuable information from a fixed number of ElectroEncephaloGraphy (EEG) channels utilizing variety of methodologies. This work analyses the different difficulties and problems associated with EEG signals for emotion identification and provides a comprehensive summary of several contemporary approaches. Pre-processing, feature extraction, and categorization are the first steps in the process of recognizing emotions from EEG signals. The main goal of this survey is to sought and enhance brain signal-based emotion detection ability by comparing all novel and adaptive channel selection technique that recognize the distinct changes in brain activities that varies between individuals and emotional states.

Deep Learning Methods for EEG Applications: Focusing on CNN and RNN

In the field of neuroscience, the electroencephalogram (EEG) study aims to discover patterns of various human brain activities in an efficient and accurate manner, which plays an important role in treating brain diseases and other fields. The emergence and development of deep learning networks bring end-to-end approaches towards processing and classifying human brain signals. In the first section of the study, two typical networks used in EEG applications—the convolutional neural network (CNN) and the recurrent neural network (RNN)—are introduced, then the study measures the performance of each network in relation to their specific characteristics in temporal and spatial domain respectively and analyzes how noise and other interference towards EEG signals affect the training of the two networks. To make an integral view of how to take advantage of both networks mentioned above for EEG applications, the paper finally introduces a general system framework using a combination of CNN and RNN. This study will bring important value to the research and application of deep learning methods for EEG applications.

A Brain‐to‐Brain Interface with a Flexible Neural Probe for Mouse Turning Control by Human Mind

We proposed a brain‐to‐brain interface (B2BI) that enabled effective control of mouse turning behavior by human mind using an implantable, ultra‐flexible neural probe. The neural probe was capable of writing in neural signals to the mouse brain, with effective electrical stimulation observed at a low current of 5 μA. Additionally, a wireless electroencephalogram (EEG) device was developed for the remote transmission of human brain signals, which were decoded by the canonical correlation analysis (CCA) method with an accuracy rate of up to 98.6%. These results demonstrate the potential of B2BI technology to enable communication and control between humans and animals. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

EEGVision: Reconstructing vision from human brain signals

Abstract The intricate mechanisms elucidating the interplay between human visual perceptions and cognitive processes remain elusive. Exploring and reconstructing visual stimuli from cerebral signals could help us better understand the processes by which the human brain generates visual imagery. However, the inherent complexity and significant noise in brain signals limit current efforts to reconstruct visual stimuli, resulting in low-granularity images that miss details. To address these challenges, this paper proposes EEGVision, a comprehensive framework for generating high-quality images directly from brain signals. Leveraging the recent strides in multi-modal models within the realm of deep learning, it is now feasible to bridge the gap between EEG data and visual representation. This process starts with a time-frequency fusion encoder in EEGVision, which quickly pulls out cross-domain and robust features from EEG signals. We then design two parallel pipelines to align EEG embeddings with image features at both perceptual and semantic levels. The process uses a stable diffusion-trained image-to-image pipeline that combines coarse and fine-grained data to get high-quality images back from EEG data. Both quantitative and qualitative assessments affirm that EEGVision surpasses contemporary benchmarks. This network architecture holds promise for further applications in the domain of neuroscience, aiming to unravel the genesis of human visual perception mechanisms. All code is accessible via https://github.com/AvancierGuo/EEGVision.

Attention-Based Multimodal tCNN for Classification of Steady-State Visual Evoked Potentials and Its Application to Gripper Control.

The classification problem for short time-window steady-state visual evoked potentials (SSVEPs) is important in practical applications because shorter time-window often means faster response speed. By combining the advantages of the local feature learning ability of convolutional neural network (CNN) and the feature importance distinguishing ability of attention mechanism, a novel network called AttentCNN is proposed to further improve the classification performance for short time-window SSVEP. Considering the frequency-domain features extracted from short time-window signals are not obvious, this network starts with the time-domain feature extraction module based on the filter bank (FB). The FB consists of four sixth-order Butterworth filters with different bandpass ranges. Then extracted multimodal features are aggregated together. The second major module is a set of residual squeeze and excitation blocks (RSEs) that has the ability to improve the quality of extracted features by learning the interdependence between features. The final major module is time-domain CNN (tCNN) that consists of four CNNs for further feature extraction and followed by a fully connected (FC) layer for output. Our designed networks are validated over two large public datasets, and necessary comparisons are given to verify the effectiveness and superiority of the proposed network. In the end, in order to demonstrate the application potential of the proposed strategy in the medical rehabilitation field, we design a novel five-finger bionic hand and connect it to our trained network to achieve the control of bionic hand by human brain signals directly. Our source codes are available on Github: https://github.com/JiannanChen/AggtCNN.git.

High-resolution neural recordings improve the accuracy of speech decoding

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.

A robust brain pattern for brain-based authentication methods using deep breath

Security authentication involves the process of verifying a person's identity. Authentication technology has played a crucial role in data security for many years. However, existing typical biometric authentication technologies exhibit limitations related to usability, time efficiency, and notably, the long-term viability of the method. Recent technological advancements have led to the development of specific devices capable of reproducing human biometrics due to their visibility and tactile nature. Consequently, there is a demand for a new biometric method to address the limitations of current authentication systems. Human brain signals have been utilized in various Brain-Computer Interface (BCI) applications. Nevertheless, this approach also faces challenges related to usability, time efficiency, and most importantly, the stability of the method over time. Studies reveal that the stability of brain patterns poses a significant challenge in EEG-based authentication techniques. Stability refers to the capacity to withstand changes or disruptions, while permanency implies a lasting and unchanging state. Notably, stability can be temporary and subject to fluctuations, whereas permanency suggests a more enduring condition. Research demonstrates that utilizing alpha brainwaves is a superior option for authentication compared to other brainwave types. Many brain states lack stability in different situations. Interestingly, deep breathing can enhance alpha waves irrespective of the brain's current state. To explore the potential of utilizing deep breathing as a security pattern for authentication purposes, an experiment was conducted to investigate its effects on brain activity and its role in enhancing alpha brainwaves. By focusing on bolstering the permanency of brain patterns, our aim is to address the challenges associated with stability in EEG-based authentication techniques. The experimental results exhibited a high success rate of 91 % and 90 % for Support Vector Machine and Neural Network classifiers, respectively. These results suggest that deep breathing not only enhances permanency but could also serve as a suitable option for a brainwave-based authentication method.

The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network.

A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm.

Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.

Motor Imagery-based BCI Perspective in Procedure and Analysis

Motor Imagery (MI) is a cognitive exercise that can be utilized in motor rehabilitation. Using brain-computer interfaces (BCIs) is a practical technique to link computers and human brains, which can acquire and understand human brain signals when performing. MI-BCI has been proven effective to help the paralyzed regain basic movement controls. However, some problems are hindering the development of MI-BCI. Predecessors have proposed relevant methods to resolve them. Thus, it is important to learn about MI-BCI comprehensively and understand some novel methods to improve the performance of MI-BCI. Collecting and reviewing other essays, the study focuses on the structure and lists new applications of MI-BCI especially about the use of deep learning. The study also discusses self-paced training during the calibration phase and deep learning approaches for potential fields. Finally, the analysis of applications and four possible future directions are posed in the discussion, hoping to offer some useful advice to improve MI-based BCI.

Electromagnetic Brain–Computer–Metasurface Holography

As an important bridge between the human brain and external devices, the brain–computer interface (BCI) has received much attention for decades. To further explore the connection between the human brain signals and electromagnetic (EM) information, here we propose a brain–computer–metasurface holography (BCMH) system to enable the brain control of EM imaging. By using a P300-based BCI and a 1-bit programmable metasurface, BCMH can precisely reproduce specific EM holographic images according to the operator’s intentions. Twenty distinct images are designed and tested with BCMH, showing good consistency with the theoretical expectation. The proposed BCMH, integrating the EM manipulation capability with intelligence of the human brain, may provide a new paradigm of interactions among the human, intelligent metasurfaces, and machines, and hence promote the visual-reality (VR) technology.

Neurocybernetic Assistive Technologies to Enhance Robotic Wheelchair Navigation

Millions of individuals worldwide face mobility impairments and rely on wheelchairs for assistance. Yet a significant portion of individuals with severe motor disabilities or insufficient familiarity with conventional interfaces cannot consider powered wheelchairs as a viable solution. Neurocybernetics present a promising solution to this challenge. Neurocybernetics involves exploring how the nervous system interacts with systems, like computers and robots. Through the use of interfaces users can control wheelchairs using neural signals from their brains making navigation more intuitive and effective. Assistive technologies, especially robotic wheelchairs powered by sensors and control systems, are essential for those disabled people, with mobility issues, to live more independently and better lives in their daily life even without the assistance of others. The primary objective of this study is to improve the quality of life for individuals with disabilities who depend on such assistance for their mobility requirements. This article examines some of the cutting-edge neurocybernetic devices that assist people with disabilities, such as brain-computer interfaces (BCIs), algorithms to understand brain signals, and adaptive control methods for wheelchairs. This study proposes a robotic wheelchair which utilizes the human brain signal and undergoes machine learning based classification model with neuromorphic adaptive control. It discusses the key considerations, difficulties, and potential uses of neurocybernetic robotic wheelchairs in real life situations. Additionally, it addresses the ethical, legal, and societal implications of using these technologies and suggests future research areas to advance their creation and use.

Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset

Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset. CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18–88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.

Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence

The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain–Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlike existing technologies that provide a reliable comparison of different AI-based classification algorithms. This paper also predicts a reliable bandwidth for the BCI process and provides exact placements of EEG electrodes to verify different arm moments. We have applied different classification algorithms, i.e., Random Forest, KNN, Gradient Boosting, Logistic Regression, SVM, and Decision Tree, to four different users. The accuracy of all prescribed classifiers has been calculated by considering the first user as a reference. The presented results validate the novel deployment, and the comparison shows that the accuracy for Random Forest remained optimal at around 76%, Gradient Boosting is around 74%, while the lowest is 64% for Decision Tree. It has been observed that people have different activation bandwidths while the dominant frequency varies from person-to-person that causes fluctuations in the EEG dataset.

Feature Extraction of EEG Signals for Seizure Detection Using Machine Learning Algorthims

Epilepsy is a central nervous system disorder in which brain activity becomes abnormal and causes periods of unusual behavior and sometimes loss of awareness. Epilepsy is a disease that may affect males or females of all ethnic groups and ages. Detecting seizures is challenging due to the difference in human behaviors and brain signals. This paper aims to automate the extraction of electroencephalogram (EEG) signals without referring to doctors using two feature extraction methods, namely Wavelet Packet decomposition (WPD) and Genetic Algorithm-Based Frequency-Domain Feature Search (GAFDS). Three machine learning algorithms were applied, namely Conventional Neural Networks (CNNs), Support Vector Machine (SVM), and Random Forest (RF) to diagnose epileptic seizures. The results achieved from the classifiers show a higher accuracy rate using CNNs as a classifier and GAFDS as feature extraction reaching 97.93% accuracy while the accuracy rate of the SVM and RF was 94.49% and 88.03% respectively.

EEG-based affective state recognition from human brain signals by using Hjorth-activity

EEG-based emotion recognition enables investigation of human brain activity, which is recognized as an important factor in brain-computer interface. In recent years, several methods have been studied to find optimal features from brain signals. The main limitation of existing studies is that either they consider very few emotion classes or they employ a large feature set. To overcome these issues, we propose a novel Hjorth-feature-based emotion recognition model. Unlike other methods, our proposed method explores a wider set of emotion classes in the arousal-valence domain. To reduce the dimension of the feature set, we employ Hjorth parameters (HPs) and analyze the parameters in the frequency domain. At the same time, our study was focused to maintain the accuracy of emotion recognition for four emotional classes. The average accuracy was approximately 69%, 76%, 85%, 59%, and 87% for DEAP, SEED-IV, DREAMER, SELEMO, and ASCERTAIN, respectively. Results show that the features from HP activity with random forest outperforms all the classic methods of EEG-based emotion recognition.

Editorial: Improving Neuroprosthetics Through Novel Techniques for Processing Electrophysiological Human Brain Signals

Editorial: Improving Neuroprosthetics Through Novel Techniques for Processing Electrophysiological Human Brain Signals

Cholesterol and matrisome pathways dysregulated in astrocytes and microglia

The impact of apolipoprotein E ε4 (APOE4), the strongest genetic risk factor for Alzheimer's disease (AD), on human brain cellular function remains unclear. Here, we investigated the effects of APOE4 on brain cell types derived from population and isogenic human induced pluripotent stem cells, post-mortem brain, and APOE targeted replacement mice. Population and isogenic models demonstrate that APOE4 local haplotype, rather than a single risk allele, contributes to risk. Global transcriptomic analyses reveal human-specific, APOE4-driven lipid metabolic dysregulation in astrocytes and microglia. APOE4 enhances de novo cholesterol synthesis despite elevated intracellular cholesterol due to lysosomal cholesterol sequestration in astrocytes. Further, matrisome dysregulation is associated with upregulated chemotaxis, glial activation, and lipid biosynthesis in astrocytes co-cultured with neurons, which recapitulates altered astrocyte matrisome signaling in human brain. Thus, APOE4 initiates glia-specific cell and non-cell autonomous dysregulation that may contribute to increased AD risk.

Brain Waves Signal Modeling for Object Classification Using Random Forest Method

In this research, the connection between human vision information and simultaneous brain signal is studied; to this end, an experiment has been made. Clearly, brain wave signals are captured in the situation that the participants are looking at the specific object. More precisely, the brain signals of 9 shapes are recorded for each participant. Also, 9 participants voluntarily have been involved in the experiment. Then, the collected signals are organised into training and testing groups. After that Random Forest classifier is used to classify the signals.
 The accuracy results demonstrate a connection between human vision information and simultaneous brain signal. Overall accuracy for all shapes as separated as per cases is 20.48%, and for shapes, numbers 6 and 8 are 55.34% 36.57%, respectively. It can be concluded that human brain signals can be categorised based on human vision inputs.