A Low-Cost Intelligent 3D-Printed Prosthetic Arm Controlled Exclusively by EEG Signals Using Adaptive Machine Learning

In an ideal future limb function can be restored after suffering any kind of damage from an accident or a disease. The dream of fully functional prosthetic limbs depicts a great opportunity towards this vision. One of the main challenges is to create a control algorithm which processes physiological signals of the human body and moves the prosthetic limb according to the user's intentions. Brain-Computer Interfaces (BCIs) are popular subjects of research worldwide in the view of prosthesis control. Building mostly on EEG (electroencephalogram) signals, such systems must perform a challenging task - extracting control information from a signal with extremely low signal-to-noise ratio. Various digital signal processing and machine learning algorithms have been published in the past few years, but there is still no clear candidate for the title of the best one for e.g. detecting hand movement intention. In this paper various algorithms are compared on the example of detecting hand movement from EEG signals in real-time. The first goal of this paper is to present a couple of promising methods from other researchers in the past years, along with demonstrating the performance of other algorithms on a certain grasp-and-lift task. Another goal of the paper is to outline a future direction for the research of movement intent detection algorithms.

Brain Computer Interfaces (BCIs) have several applications. One of the most crucial domains that BCIs cater to is prosthetics, orthotics and rehabilitation devices. Such devices aim to better the lives of specially-abled people. Motor Imagery or Movement Intention (MI) neural signals are collected to obtain a better insight of the underlying neural activities. EEG is the most popular modality for obtaining neural signals due to its advantages like non-invasiveness, portability, and the ease of use. The utilization of MI EEG signals to design BCIs has been heavily exploited. Spectral estimation methods have been exploited to delineate different MI tasks. This article focusses on exploring channel-wise temporal and spectral features of MI EEG signals. A rich MI EEG dataset involving five different motor imageries has been utilized in this study. The analysis based upon channel-wise temporal and spectral decomposition involves two major biomarkers, Event Related Spectral Perturbation (ERSP) and Inter Trial Coherence (ITC). ERSP can be considered as a generalization of power increase and decrease within a frequency range of interest. ITC reflects consistency of rhythms across the trials. This article aims to exploit ERSP and ITC methods to explore relevant time segment, frequency range and informative channels associated to MI signatures.

Due to the growing commercial exploitation of WiFi-based technologies in recent years and the lack of solutions for effective WiFi orchestration, spectrum utilization and user performance are often sub-optimal. The integration of WiFi-based radio resource management (RRM) and radio environmental maps (REMs) may create a cost-effective Smart-WiFi solution that optimizes underlying spectrum utilisation and network performance. The REM enables effective use of radio environmental data such as device location, estimated channel models, real-time network interference levels, WiFi channel occupancies, and so on. In WiFi-related settings, this information may be used to make intelligent and optimum RRM decisions. This study provides a new REM-based RRM strategy for managing and optimizing commercial WiFi devices that makes use of the underlying radio environmental data. The research uses a commercially available platform to illustrate the suggested solution, which includes on-the-fly radio environmental data gathering and optimal WiFi RRM allocation. In comparison to traditional WiFi networks, the simulation study findings reveal that the proposed Smart-WiFi leverages considerable performance advantages for large-scale situations.

EEG signal is one of the biological signals that can be useful to control wearable robotic devices, according to the human motion intention. However, the real-time estimation of the user's motion intention from EEG signals is cumbersome. The user's motion intention might not be estimated when the user does not concentrate on the control of the robot, distracted by other things or disturbed by the outside interferences. In this paper, a neural network based real-time estimation method is proposed to detect human motion intention in terms of intended task, using EEG signals. The inputs of bandpower time series signals let the neural network identify the dynamic nature of the tasks performed. Experimental details, methodology and the prediction results are presented.

Brain–Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder’s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor–extensor muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder’s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (±0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.

It may not an easy task for physically weak elderly, disabled and injured individuals to perform the day to day activities in their life. Therefore, many assistive devices have been developed in order to improve the quality of life of those people in which they may not depend on others. Especially upper-limb power-assist exoskeletons have been developed since the upper limb motions are very important for the daily activities. Electromyography (EMG) signals and/or force sensor based control methods have been identified as the promising methods to control such exoskeleton devices. However, if the user cannot generate sufficient muscle signals or movements, the EMG or force sensor based methods could not be useful to the user. On the other hand, electroencephalography (EEG) signals are also important biological signals to extract the user's motion intention. In this study, the user's elbow joint motion is estimated based on the EEG signals. The measured EEG signals are pre-processed and input to a time-embedded linear model, which is assumed to decode the elbow joint angular velocities. The genetic algorithm (GA) is used to train the model. A six fold cross validation process was performed for each motion segment of each subject. The experimental results suggest that EEG signals with the tested decoding model can be used to continuously decode the elbow joint velocity.

BackgroundFor severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home.ResultsAfter the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (p < 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%).ConclusionsThese results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.

Investigation of the diagnosis of human brain through the electroencephalograph (EEG) is an important application of EEG signals. While automated techniques exist for EEG analysis, it is likely that additional information can be extracted from EEG signals through the use of new methods. In this paper, we propose a method that applies artificial neural networks approach for identification of the inert region from EEG signals. The method has three main steps. First, a large scale, complex EEG signals is simply normalized into a reasonable data. Second, characteristic feature from EEG signals, the normalized EEG data has no regular rule and is still so complex that neural network can not learn the characteristic feature from EEG. Hence we extract characteristic feature from EEG and use principal component analysis (PCA) and root mean square (RMS) processes in EEG data. Finally, neural network is supplied as the input value to these processes in EEG and learn these characteristic feature. To demonstrate the effectiveness of the method, we perform simulations on location of inert region from EEG data, consists of 10000 training and test data. These EEG estimation tasks were created by using a set of calculated, artificial EEG signals based on a number of current dipoles. The experimental results indicate that the proposed method has several attractive features. 1) The method can estimate the EEG feature of inert region and better generalization performance can be achieved than non preprocessing EEG. 2) The more larger inert region were, the more small estimation error become.

Emotion recognition using EEG signals is a critical task with wide-ranging applications in mental health monitoring, disorders of consciousness, and neurofeedback systems. However, EEG signals are inherently noisy and high-dimensional, posing persistent challenges to accurate emotion classification. Most existing approaches rely on limited preprocessing or employ Principal Component Analysis (PCA) solely for dimensionality reduction, often overlooking the residual artifacts that degrade classifier performance. This study introduces a novel PCA-RF framework that repurposes PCA for dual objectives: targeted noise suppression and feature dimensionality reduction. Uniquely, PCA is applied after frequency-specific filtering to more effectively eliminate residual ocular and cardiac artifacts, thus improving the quality of EEG feature representations. These compact and denoised features are then processed by a Random Forest (RF) classifier, which robustly captures the nonlinear dynamics of the EEG data. Evaluated on the DEAP dataset, the proposed PCA-RF approach achieved state-of-the-art performance, with 96.57% accuracy for arousal and 96.03% for valence classification. The key contribution of this work lies in its strategic integration of PCA for both artifact suppression and feature optimization, setting it apart from conventional pipelines and delivering a reliable and accurate solution for EEG-based emotion recognition.

Face detection is an important part of any face recognition system and it is a difficult task to detect faces of arbitrary size, orientation and location. Fusion is a technique which combines the results from two or more distinct methods in such a way that better results are obtained. We propose two different fusion techniques, data fusion and sensor fusion, that combine statistical and shape based methods. Four different features, eigenweights from Principal Component Analysis (PCA), Legendre Moments (LM), Zernike Moments (ZM) and Generic Fourier Descriptor (GFD), are used for our fusion methods. In data fusion technique, the calculated eigenweights, moments, and descriptors are concatenated together to form a feature vector. This combined feature is then classified as face or non-face pattern using Support Vector Machine (SVM). In the case of sensor fusion technique, each of the feature vectors obtained by individual methods are first classified using individual SVMs. Then the class label obtained from the individual SVMs are concatenated together to form a feature vector. This feature vector is then classified using another SVM. Both the techniques produced better results compared with the individual methods. For a database containing 300 face images and 200 non-face images, both of our methods achieved 0% false rejection rate. 99.75% accuracy was achieved for data fusion method, while sensor fusion method produced 99.9% accuracy.

This study aims to analyze the relationship between brainwave activity and the learning readiness of students with disabilities by utilizing EEG (Electroencephalography) signals. EEG signals are used to detect the brain's electrical activity that reflects mental states, including focus, concentration, and readiness to receive learning. This study was conducted at SLB 'Aisyiyah Krian with a quantitative approach through EEG signal measurement before and during the learning process. The results showed a significant correlation between the dominance of alpha and beta waves with learning readiness, while the dominance of theta and delta waves indicated unpreparedness. These findings provide a foundation for the development of a more inclusive, objective, and adaptive neurotechnology-based learning approach for students with disabilities.

The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain–computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user’s brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to learn and adapt to new technologies such as BCIs, but both (human brain and BCI) demonstrate adaptability in their responses. The human brain is skilled at quickly switching between tasks and regulating emotions, while BCIs can modify signal-processing algorithms to accommodate changes in brain activity. Furthermore, the human brain and BCI participate in knowledge acquisition; the first one strengthens cognitive abilities through exposure to new experiences, and the second one improves performance through ongoing adjustment and improvement. Current research seeks to incorporate emotional states into BCI systems to improve the user experience, despite the exceptional emotional regulation abilities of the human brain. The implementation of BCIs for older adults could be more effective, inclusive, and beneficial in improving their quality of life. This review aims to improve the understanding of brain–machine interfaces and their implications for mental health in older adults.

The amputees nowadays usually wear a prosthetic arm which are not functionable. These prosthetic arms unable to help them to carry out their basic daily work independently. However, the EEG based brain wave controlled intelligent prosthetic arm is able to assist the amputees to carry out basic daily work. By using the EEG brain wave controlled, the amputees can control the movement of the prosthetic arm by visualizing the movement of the arm. Besides the amputees, the paralyzed person can also use their thought to control the prosthetic arm. The EEG based brain wave controlled intelligent 3D printed prosthetic arm is designed to use the amputee's brain wave to control the movement of the prosthetic arm. The amputee needs to be trained in order to control every arm movement correctly. The EEG signal needs to be collected from the amputee on visualizing on 4 left forearm movements which are up, down, left and right. After that, signal pre-processing and Discrete Wavelet Transform (DWT) is performed on the EEG signal to obtain the alpha wave. Then, FFT is performed to the alpha wave to convert it to frequency-domain. The two-stage binary classifier that used Support Vector Machine (SVM) machine learning technique is created in the MATLAB to classify the alpha wave of the arm movement accurately for the user.

The current work on emotion recognition considering EEG signals has attracted significant attention due to its advantages in fields like mental health, neural computing, and human-computer interaction. Nonetheless, accurately classifying emotions using EEG signals poses a challenge, mainly due to issues such as noise, artefacts, and high dimensionality found within EEG data. Moreover, the current problem lies in extracting meaningful emotional features while efficiently addressing the above-described challenges to improve classification accuracy. Hence, this work proposed an approach for solving the challenges in emotion classification by presenting a model that integrates Principal Component Analysis (PCA) and Convolutional Neural Network (CNN). The PCA was used for removing noise and for dimensionality reduction. In contrast, the CNN was used for extracting emotion-related features and classifying emotions, mainly the two classes, arousal and valence. The proposed PCA-CNN model was evaluated considering the DEAP dataset, where the PCA-CNN model achieved 93.58% accuracy for arousal and 92.75% for valence for subject-dependent and 81.40% accuracy for arousal and 79.84% for valence for subject-independent. The findings also show that the PCA-CNN model provided better results in handling noise and reducing data complexity, ensuring high classification performance. The novelty of the PCA-CNN model is that PCA was used for preprocessing the EEG data, and CNN was used for feature optimization, which created a more efficient emotion classifier.

Electroencephalography (EEG) signals are often employed in modern BCI (brain-computer interface) systems. For patients with irreversible nerve injury, motor imagery BCIs enable them to control devices such as artificial limbs simply with brain activities (i.e. imagination) without physical movement. In practice, EEG signals are measured with multiple electrodes and often contaminated with various kinds of artifacts and noise. In this study, a novel EEG motor imagery classification approach is developed based on discrete wavelet transform (DWT) and TSK (Tagaki-Sugeno-Kang) fuzzy inference neural networks. By using energy ratios in the frequency bands that are related with motor imagery activities, fewer features are needed for the classifier, which makes the real-time implementation of BCI systems more feasible. DWT not only provides multi-resolution analysis for non-stationary EEG signals, but also aids for noise and artifact reduction. Furthermore, the TSK fuzzy neural network is able to handle imprecise information with adaptive learning. The proposed algorithm is tested on EEG signals of PhysioNet database and compared with other methods. Satisfactory computer simulation results are obtained.