EEG-Based Brain Wave Controlled Intelligent Prosthetic Arm

More natural and intuitive control is expected to maximize the auxiliary effect of the powered prosthetic leg for lower limb amputees. In order to realize the stable and flexible walking of prosthetic legs in different terrains according to human intention, a brain-computer interface (BCI) based on motor imagery (MI) is developed. For the raw electroencephalogram (EEG) signals, discrete wavelet transform (DWT) is utilized to extract the time-frequency domain features, which are used as the input signals of the common spatial pattern (CSP) to obtain the time-frequency-space domain features of EEG signals. Then, a support vector machine (SVM) classifier and a directed acyclic graph (DAG) structure are combined to classify multiclass imaginary tasks. According to the result of human intention recognition, the prosthetic leg performs the corresponding gait trajectory generated by coding the ground reaction force (GRF). In addition, a sensory feedback loop is established by functional electrical stimulation (FES), which feeds back the movement of the prosthetic leg to human in real time. The effectiveness and feasibility of the developed EEG-based volitional control of powered prosthetic legs have been validated by three subjects, all of whom were able to fulfill smoothly walking on the floor, ascending stairs, and descending stairs according to their own intentions using prosthetic legs.

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.

The Brain is the main controller of the human body consisting of neurons which generate electrical signals to control the body. Different neurological diseases can cause brain abnormalities such as ALS that affect the electrical activity of the brain which can be seen in the EEG signal. Therefore, these abnormalities can be detected by analyzing the EEG signal. As the Alpha and Beta waves are recorded during the active state of the brain, the effect of abnormalities can be seen in the waves. This study proposes a technique to detect the brain abnormalities and detect ALS syndromes based on Alpha and Beta waves. In this study, the Alpha and Beta waves are extracted from the raw EEG signal by discrete wavelet transformation. Here, the raw EEG signal is divided into several orthogonal wavelets using Doubechis 4 wavelet transform; Alpha and Beta wave frequency components are selected and reconstructed. Then these waves can be compared between different types of ALS patients and normal people based on amplitude and event related potential. The result showed great variation in amplitude as well as event related potential for different ALS patients and normal people. The proposed technique could be used in detecting abnormalities and their severity. It could also help in detecting ALS at a primary stage.

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis.

In this study, normal (healthy), pre-seizure and seizure states were analyzed using EEG data from BONN database. In this paper, we propose a new method of using SVM, KNN and Decision Tree for classification analysis in order to improve the detection accuracy of seizure. First, due to the presence of significant noise in the EEG signals, for signal pre-processing we performed noise removal. Second, two methods of frequency space transformation were used such as Discrete Wavelet Transform (DWT), separating signal into sub-bands and Empirical Mode Decomposition (EMD) technique to decompose signal into the Intrinsic Mode Functions (IMFs). Before the classification, the statistical moments of the signals in the frequency domain were obtained for feature extraction. Using these features, the performances of Support Vector Machines (SVM), Decision Tree, K-nearest Neighborhood (KNN) classifiers were analyzed. The experiment results show that the most accurate detection of epilepsy was obtained by applying EMD method with classifiers SVM, KNN and Decision Tree, and such algorithm with EMD can achieve accuracy for normal, pre-seizure, and seizure equal to 100%, 100% and 96.67%, respectively.

This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) with Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, Principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the main features in low dimensional feature space. Three classifiers name: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) classifier have been used in the proposed work for classifying the EEG signals. The raised method is tested over Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.

This paper presents a methodology for slip detection in prosthetic hand grasping by using the discrete wavelet transform (DWT) analysis. To measure the distributed three-axis contact force between the finger and objects, a wearable tactile sensor array with a spatial resolution of 5 mm is designed and fabricated. This sensor array features high flexibility and can be easily worn in a prosthetic hand, and utilized for grasping force measurement. After obtaining contact force in all nine sensing units, the DWT was used to analyze the force data for slip detection. Results showed that slip occurrence in grasping could be identified by setting the proper threshold values in z-, x-, and y-axes. Also, the position of slip occurrence at prosthetic hand finger can be determined based on the developed tactile sensor array. Thus, the developed wearable tactile sensor array may be utilized for real-time slip occurrence detection, including slip speed, position, or direction, by using the DWT analysis.

Classification of resting and cognitive states has its importance in brain neuroscience for understating the underlying behaviors of cognition. The human brain is considered as a complex system having different mental states such as resting, active or cognitive states. It is a well-understood fact that the brain activity increases with the increased demand of cognition. The deep learning algorithm based on Pre-trained convolutional neural network (CNN) networks have been used as a transfer learning for the classification of rest and cognitive states and also assessed the cognitive load using brain waves particularly alpha wave. EEG data were collected from 34 human participants at resting and during a learning state. The important EEG electrodes were selected based on the effective connectivity method and after preprocessing, EEG data has been segmented into equal number of segments using selected electrodes to investigate the deep temporal information for cognitive load assessment. The brain waves were extracted using discrete wavelet transform (DWT) for each segment and fed these segments to proposed model for classification and assessment of cognitive load. The results shows that alpha brain wave produced consistent behavior using for all cognition tasks based on pre-trained CNN models for classification and cognitive load assessment.

The opioid epidemic in the United States has prompted the Department of Health and Human Services to advocate pain management as a crucial strategy. An essential aspect of this approach involves tailoring treatment based on individual pain levels, which, in turn, determines the use of anesthetics before or after surgery. Accurately measuring a patient's pain intensity is vital for both physicians and patients. However, current assessment methods, such as the visual analog scale (VAS), numerical rating scale (NRS), and verbal rating scale (VRS), rely on subjective self-reporting, and thus lack consistency. Therefore, developing an objective pain assessment system is imperative to overcome the shortcomings of these survey-like approaches.In this dissertation, we aim to use physiological signals and facial expressions to objectively assess pain. To achieve this goal, we conducted cold pain experiments at Northeastern University and Quantitative Sensory Testing at Brigham and Women's Hospital. Besides healthy subjects, low-back pain patients were also recruited. Four studies were investigated. In the first study, facial expressions from 29 experiment subjects were collected. Three different models (Inception V3, VGG-LSTM, and Convolutional LSTM) were used to estimate three intensities of cold pain. It demonstrates the potential for the estimation of cold pain intensity from facial expression analysis and shows that the personalized spatial-temporal framework has better performance in cold pain intensity estimation. In the second study, the fusion of facial expression and EEG signals for the pain intensity estimation was investigated. Four features of EEG signals from five frequency bands on 32 channels were extracted. Action Units (AUs) were extracted as the features of facial expression. Feature level fusion was used to train a Deep Belief Network (DBN). The results showed that the fusion of facial expression and EEG based on machine learning technique could be a promising way to objectively assess cold pain intensity. In the third study, augmented common spatial patterns (ACSP), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) were applied to perform the EEG-based pain intensity estimation task. The outcome showed that the hybrid system of the ACSP-CNN-SVM achieved higher pain intensity estimation accuracy. In the fourth study, an objective pain intensity estimation using EEG and pupillary response based on machine learning models was proposed. Two fusion strategies were investigated and SVM was utilized to estimate the pain intensities. The results showed that the EEG signals together with pupil diameter based on machine learning technique could be a promising way to objectively assess pain intensity. --Author's abstract

The present research is about the various mental tasks, experienced by humans with cognitive function disorders, classified using discrete wavelet transform (DWT) and support vector machine (SVM). The electroencephalogram (EEG) database was obtained from online brain-computer interface (BCI) competition paradigm III and offline B-alert EEG machine was from CARE Hospital, Nagpur. EEG signals from paralysed patients decomposed into the frequency sub-bands using DWT and a set of statistical features extracted from the sub-bands represent the distribution of wavelet coefficients used to reduce the dimension of data, features applied to SVM for classification of left hand and right hand movement. With this system, classification of EEG signals was done with accuracy 91.66% for BCI competition paradigm III and 97% for B-alert machine.

Human cognitive state classification using Electroencephalogram (EEG) signal is one of the dynamic exploring regions in emerging smart machine systems. It motivates to build estimator for revealing the human cognitive behaviours for different environments. In this work, we present an operational human cognitive behaviour detection system based on ambulatory EEG signal analysis. A novel event driven environment is created using external stimuli to collect those signals. They are captured using 14 channel Emotiv neuro-headset. Mel-Frequency Cepstral Coefficients (MFCC) is a very popular feature extraction method in acoustic signal processing. In spite of it’s popularity, it has not been explored much when it comes to EEG signals. This paper proposes a novel technique of applying MFCC feature extraction method for ambulatory EEG signals and the results are found to be promising. Besides other feature extraction methods such as: Power Spectral Density (PSD), Discrete Wavelet Transformation (DWT) are used to extract intrinsic features from EEG signal. A statistical technique, Fisher Discriminant Ratio (FDR) is applied on them to evaluate the robustness of each feature. A higher FDR value is observed from MFCC features which demonstrate its discriminative power during classification. Different machine learning techniques such as Probabilistic neural network (P-NN), k-nearest neighbor (kNN), multi-class support vector machine (MCSVM) are applied for training, testing and validating the extracted features. A comparative analysis is discussed for each classifier coupled with each feature. It is found that P-NN with MFCC feature produces the maximum accuracy of 92.61% amongst all the remaining classifiers.

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.

Human brain known commonly as an individual's computer is one of the complex organs in the body. The brain serves to control and coordinate the mental and physical actions. It has the capacity to store innumerable data. This data includes not only childhood memories but also those related to the present age. Tracking the electrical potentials or impulses, commonly known as brain waves, can be utilized for various purposes ranging from diagnosing diseases to decoding personality traits. This can be done using the technique of Electroencephalography (EEG). The amount of data available is exploding so also the opportunities of data mining thereby mounting privacy risks. The current practice either prevents the reuse of raw EEG signals or disrespects participants' right to privacy by using this data for purposes other than those consented to, by the individual. The aim of this work is to put a stop to such privacy risks by extracting and transferring only those features related to an application. Feature is a specific characteristic measurement of a pattern segment. EEG signal processing pertains mainly to feature extraction. There are different methods for extracting features from EEG signals. In this work, the features related to neurological diseases, namely epilepsy and Alzheimer's disease, are extracted using Discrete Wavelet Transform (DWT) and those related to Schizophrenia are extracted using Autoregressive (AR) method. For securing the raw data, it is doubly encrypted using Advanced Encryption Standard (AES) algorithm and is kept under the control of each user. The proposed system thus ensures the privacy of an individual's EEG signals, and mitigates any risk that may arise if raw data is made available as such to the public.

The classification of electroencephalogram (EEG) signals is a key technique of brain–computer interface (BCI) system. In view of the complexity of EEG signals and the low accuracy in EEG signals recognition, a motor imagery EEG signals classification method with multi-domain fusion based on Dempster–Shafer (D-S) evidence theory is presented in this paper. Firstly, time domain statistics (TDS), autoregressive (AR) model and discrete wavelet transform (DWT) are used to extract features from EEG signals, respectively, and three probabilistic output support vector machine (SVM) classification models are trained based on these three feature sets. Secondly, using the output of each SVM, we construct basic probability assignment (BPA) function and get fusion BPA through D-S evidence theory. Finally, determining the class of test samples based on decision rules. Four databases from BCI competition are employed to evaluate the proposed approach, and the highest classification accuracy reaches 92.83%. Results show that this method acquires higher accuracy and has strong individual adaptability.