SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts, high variance in seizure semiology among epileptic patients, and limited availability of clinical data. To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for cross-patient seizure type classification. We used the recently released TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross validation for scalp EEG based multi-class seizure type classification. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints

1 - A Comprehensive Study of Motor Imagery EEG-Based Classification Using Computational Analysis

Brain computer interfaces (BCI) are systems that integrate a user’s neural features with robotic machines to perform tasks. BCI systems are very unstable still due to Electroencephalography (EEG) having interference from unanticipated noise. Using Independent Component Analysis (ICA), a novel variable threshold model for noise feature extraction. The de-noised EEG data is classified with a high accuracy of more than 94% when using artificial neural networks. The effectiveness of the proposed variable threshold model is validated by the significant reduction in the variance of user classification accuracy across multiple sessions. Nonetheless, based on the variance and classification, subjects are further categorized into two groups. The lower classification accuracy group is found to have an increased variance in its classification accuracies. To confirm these results, a Kaiser Windowing technique is used to compute the signal-to-noise ratio (SNR) for all subjects and a low SNR is obtained for all EEG signals pertaining to the group with the low classification accuracy. This study not only establishes a direct relationship between classification accuracy, classification accuracy variance, and SNR, but also presents classification results that are significantly higher than the accuracies reported by prior studies using the same EEG dataset

Determining States of Movement in Humans Using Minimally Processed EEG Signals and Various Classification Methods

Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without taking biofeedback such as brain state into account. Additionally, the administration of tDCS by a technician during motor tasks is a tiresome process. Machine learning and deep learning algorithms are often used to perform classification tasks on high-dimensional data, and have been successfully used to classify movement states based on EEG features. In this thesis, a program capable of performing live classification of motor state using machine learning and EEG as biofeedback is proposed. This program would allow for the development of a device that optimally administers tDCS dosage during motor tasks. This is achieved by surveying the literature for motor classification techniques based on EEG signals, recreating the methods in the surveyed literature, measuring their accuracy, and creating an application to perform online capturing and analysis of EEG recordings using the classifier with the highest accuracy to demonstrate the feasibility of real-time classification. The highest accuracy of motor classification is achieved by training a random forest on binned spectral decomposition from a normalized signal. While live classification was successfully performed, accuracy was limited by external changes to the recording environment, skewing the input to the trained model

Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model

Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment

The study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG). We computed different types of entropy from EEG signals and found that Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We also investigated different combinations of signal frequency ranges and EEG channels to accurately diagnose PD. Finally, with a fewer number of features (11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%. The most prominent frequency range of EEG signals has been identified, and we have found that high classification accuracy depends on low-frequency signal components (0-4 Hz). Moreover, the most informative signals were mainly received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore, we assessed the accuracy of the diagnosis of PD using three different lengths of EEG data (150-1000 samples). Because the computational complexity is reduced by reducing the input data. As a result, we have achieved a maximum mean accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By reducing the number of features and segment lengths, the computational cost of classification can be reduced. Lower-performance smart ML sensors can be used in IoT environments for enhances human resilience to PD.Comment: 19 pages, 10 figures, 2 table

EEG Signal Analysis for Effective Classification of Brain States

EEG (Electroencephalogram) is a non-stationary signal that has been well established to be used for studying various states of the brain, in general, and several disorders, in particular. This work presents efficient signal processing and classification of the EEG signal. The digital filters used during decomposition of the input EEG signal have transfer functions which are simple and easily realizable on digital signal processors (DSP) and embedded systems. The features selected in this study; energy, entropy and variance; are among the most efficient and informative to analyze the EEG signal strength and distribution for detecting brain disorders such as seizure. Training and testing of the extracted features are performed using linear kernel (Support Vector Machine) SVM and thresholding in DSP algorithms and hardware, respectively. The experimental results for the digital signal processing algorithms show a high classification accuracy of 95% in the occurrence of seizure in epileptic patients. The techniques in this work are also under investigation for classifying other brain states/disorders such as sleep stages, sleep apnea and multiple sclerosis

A Hybrid Fuzzy Cognitive Map/Support Vector Machine Approach for EEG-Based Emotion Classification Using Compressed Sensing

© 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Due to the high dimensional, non-stationary and non-linear properties of electroencephalogram (EEG), a significant portion of research on EEG analysis remains unknown. In this paper, a novel approach to EEG-based human emotion study is presented using Big Data methods with a hybrid classifier. An EEG dataset is firstly compressed using compressed sensing, then, wavelet transform features are extracted, and a hybrid Support Vector Machine (SVM) and Fuzzy Cognitive Map classifier is designed. The compressed data is only one-fourth of the original size, and the hybrid classifier has the average accuracy by 73.32%. Comparing to a single SVM classifier, the average accuracy is improved by 3.23%. These outcomes show that psychological signal can be compressed without the sparsity identity. The stable and high accuracy classification system demonstrates that EEG signal can detect human emotion, and the findings further prove the existence of the inter-relationship between various regions of the brain