Subspace techniques for task-independent EEG person identification
01 Jul 2019-Vol. 2019, pp 4545-4548
TL;DR: Novel modifications are proposed for both i-vector and x-vector frameworks to project person-specific signatures from multi-channel EEG into a subspace that enhances the biometric information and suppresses other nuisance factors.
Abstract: There has been a growing interest in studying electroencephalography signals (EEG) as a possible biometric. The brain signals captured by EEG are rich and carry information related to the individual, tasks being performed, mental state, and other channel/measurement noise due to session variability and artifacts. To effectively extract person-specific signatures present in EEG, it is necessary to define a subspace that enhances the biometric information and suppresses other nuisance factors. i-vector and x-vector are state-of-art subspace techniques used in speaker recognition. In this paper, novel modifications are proposed for both frameworks to project person-specific signatures from multi-channel EEG into a subspace. The modified i-vector and x-vector systems outperform baseline i-vector and x-vector systems with an absolute improvement of 10.5% and 15.9%, respectively.
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21 Jan 2022
TL;DR: This work extensively compare multiple state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one to evaluate more practical aspects of seizure detection models.
Abstract: Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on-device applications. In this work, for the first time, we extensively compare multiple state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models.
4 citations
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TL;DR: In this article, a deep convolutional neural network (CNN) and squeeze-and-excitation blocks (S-CNN) were used to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification.
Abstract: Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.
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05 Dec 2020
TL;DR: In this paper, a neural network system using the time-delay neural network to model temporal information and long short-term memory (LSTM) layer to model spatial information is proposed.
Abstract: Automatic detection of seizures from EEG signals is an important problem of interest for clinical institutions. EEG is a temporal signal collected from multiple spatial sources around the scalp. Efficient modeling of both temporal and spatial information is important to identify the seizures using EEG. In this paper, we propose a neural network system using the time-delay neural network to model temporal information (TDNN) and long short term memory (LSTM) layer to model spatial information. On the development subset of Temple University seizure dataset, the proposed system achieved a sensitivity of 23.32 % with 11.13 false alarms in 24 hours.
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TL;DR: In this article, a deep learning approach using siamese convolutional neural networks is employed to extract, from the considered EEG recordings, subject-specific template representations, and an extensive set of experimental tests, performed on a multi-session database comprising EEG data acquired from 45 subjects while performing six different tasks, are employed to evaluate whether it is actually possible to verify the identity of a subject using brain signals, regardless the performed mental task.
Abstract: Considerable interest has been recently devoted to the exploitation of brain activity as biometric identifier in automatic recognition systems, with a major focus on data acquired through electroencephalography (EEG). Several researches have in fact confirmed the presence of discriminative characteristics within brain signals recorded while performing specific mental tasks. Yet, to make EEG-based recognition appealing for practical applications, it would be highly advisable to investigate the existence and permanence of such distinctive traits while performing different mental tasks. In this regard, the present study evaluates the feasibility of performing task-independent EEG-based biometric recognition. A deep learning approach using siamese convolutional neural networks is employed to extract, from the considered EEG recordings, subject-specific template representations. An extensive set of experimental tests, performed on a multi-session database comprising EEG data acquired from 45 subjects while performing six different tasks, is employed to evaluate whether it is actually possible to verify the identity of a subject using brain signals, regardless the performed mental task.
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TL;DR: In this article , an automatic speech recognition technology based on deep learning techniques was developed to detect and further rectify such deficiencies, and the EEG data was prepared before degradation into numerous EEG sub-strands with a discrete wavelet transformation to eliminate unimportant errors.
Abstract: Speech difficulties are common in children and teenagers, but they can also occur in adults as a result of physical problems. A speech disorder is a situation in which an individual struggles to produce or construct the spoken sounds necessary for interpersonal communication. As a result, it could be challenging to comprehend the person's speech. Articulation abnormalities are typical speech problems. In this situation, automatic speech recognition (ASR) technology may be used to detect and further rectify such deficiencies. The first attempts to detect speech abnormalities were made in the early 1970s, and they appear to have followed the same path as those on the ASR. These early experiments did rely heavily on signal processing techniques. As time goes on, more ideas from ASR technology are being incorporated into systems that deal with speech impairments. Many traditional techniques are executed in the ASR system. In this paper, we developed an automatic speech recognition technology based on deep learning techniques. In this paper, we research alternative extraction and classification methods of electroencephalography (EEG) to help diagnose speech disorders (SD). The EEG data is prepared before degradation into numerous EEG sub-strands with a discrete wavelet transformation to eliminate unimportant errors. For sharpening signals, the Eigenvector crack curvature wavelet method was used. A hyper-similarity abnormality coder is used for feature extraction in the EEG recording and to detect synchronization between EEG channels, which may show abnormalities in communication. The recovered functions are then categorized using the Deep Residual–encoder–based VGG net CNN Classification Method. Thus, the techniques proposed to produce the most promising outcome aren't the suggested technique attained better classification accuracy when compared to the traditional methodologies.
References
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
37,868 citations
"Subspace techniques for task-indepe..." refers methods in this paper
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TL;DR: The major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs) are described.
Abstract: Reynolds, Douglas A., Quatieri, Thomas F., and Dunn, Robert B., Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing10(2000), 19Â?41.In this paper we describe the major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented.
4,448 citations
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TL;DR: An extension of the previous work which proposes a new speaker representation for speaker verification, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis, named the total variability space because it models both speaker and channel variabilities.
Abstract: This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.
3,060 citations
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TL;DR: This paper uses data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness of deep neural network embeddings for speaker recognition.
Abstract: In this paper, we use data augmentation to improve performance of deep neural network (DNN) embeddings for speaker recognition. The DNN, which is trained to discriminate between speakers, maps variable-length utterances to fixed-dimensional embeddings that we call x-vectors. Prior studies have found that embeddings leverage large-scale training datasets better than i-vectors. However, it can be challenging to collect substantial quantities of labeled data for training. We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA classifier, it is not helpful in the i-vector extractor. However, the x-vector DNN effectively exploits data augmentation, due to its supervised training. As a result, the x-vectors achieve superior performance on the evaluation datasets.
1,413 citations
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TL;DR: This paper investigates which configuration and which parameters lead to the best performance of an i-vectors/PLDA based speaker verification system and presents at the end some preliminary experiments in which the utterances comprised in the CSTR VCTK corpus were used besides utterances from MIT-MDSVC for training the total variability covariance matrix and the underlying PLDA matrices.
Abstract: It is known that the performance of the i-vectors/PLDA based speaker verification systems is affected in the cases of short utterances and limited training data The performance degradation appears because the shorter the utterance, the less reliable the extracted i-vector is, and because the total variability covariance matrix and the underlying PLDA matrices need a significant amount of data to be robustly estimated Considering the “MIT Mobile Device Speaker Verification Corpus” (MIT-MDSVC) as a representative dataset for robust speaker verification tasks on limited amount of training data, this paper investigates which configuration and which parameters lead to the best performance of an i-vectors/PLDA based speaker verification The i-vectors/PLDA based system achieved good performance only when the total variability matrix and the underlying PLDA matrices were trained with data belonging to the enrolled speakers This way of training means that the system should be fully retrained when new enrolled speakers were added The performance of the system was more sensitive to the amount of training data of the underlying PLDA matrices than to the amount of training data of the total variability matrix Overall, the Equal Error Rate performance of the i-vectors/PLDA based system was around 1% below the performance of a GMM-UBM system on the chosen dataset The paper presents at the end some preliminary experiments in which the utterances comprised in the CSTR VCTK corpus were used besides utterances from MIT-MDSVC for training the total variability covariance matrix and the underlying PLDA matrices
737 citations
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