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Wavelet filterbank-based EEG rhythm-specific spatial features for covert speech classification

IET SIGNAL PROCESSING 2022年第1期16卷 92-105页

作者： Biswas, Sukanya Sinha, Rohit Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India

The derivation of rhythm-specific spatial patterns of electroencephalographic (EEG) signals for covert speech EEG classification task is dealt in this work. This study has been performed on a publicly accessible multi-channel covert speech EEG database consisting of multi-syllabic words. With the motivation of deriving more discriminative features, each channel data has been decomposed into distinct bands focussing on the five basic EEG rhythms using the discrete wavelet transform (DWT)-based signal decomposition algorithm. Following that, for each band, the multi-class common spatial pattern (CSP) features are computed using joint approximate diagonalisation. The final feature vector is formed by retaining a few significant CSP components from all five bands. Radial basis function kernel-based support vector machines are used for covert speech classification. After 5-fold cross-validation, the proposed DWT-based bandwise-CSP features are noted to yield an average classification accuracy of 94%. In contrast with the existing (non-decomposed) CSP feature, a relative improvement of about 24% is achieved. For generalisation purposes, the proposed approach has also been evaluated for another covert speech database comprising more classes and subjects. The study highlights the discovery of more discriminative patterns with rhythm-specific processing in the context of covert speech classification. The proposed approach has the potential to be useful in other brain-computer interface paradigms that employ EEG signals.

关键词： electroencephalography rhythm-specific processing medical signal processing wavelet transforms wavelet filterbank-based average classification accuracy covert speech classification final feature vector brain-computer interfaces discrete wavelet transforms radial basis function kernel-based support vector machines discriminative features covert speech database basic EEG rhythms feature extraction multiclass common spatial pattern features signal classification rhythm-specific spatial patterns pattern classification support vector machines multisyllabic words classification task DWT-based bandwise-CSP features publicly accessible multichannel covert speech existing CSP feature

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AN INSIGHT INTO VIABLE MACHINE LEARNING MODELS FOR EARLY DIAGNOSIS OF CARDIOVASCULAR DISEASE

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第1期25卷 187-203页

作者： Chalapathi, Mukkoti Maruthi venkata Vali, Dudekula Khasim Kumar, Yellapragada Venkata Pavan Reddy, Challa Pradeep Kasaraneni, Purna Prakash VIT AP Univ Sch Comp Sci & Engn Amaravati 522237 Andhra Pradesh India VIT AP Univ Sch Elect Engn Amaravati 522237 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Comp Sci & Engn Vaddeswaram 522302 Andhra Pradesh India

Cardiovascular diseases (CVD) are a prominent source of death across the globe, and these deaths are taking place in low-to middle-income nations. Due to this, CVD prevention is a pressing issue that has already been the subject of extensive research. Innovative methodologies in machine learning (ML) can have a greater impact on the diagnosis of CVD, yet the research on CVD is more challenging and attracting more research indeed. In this paper, we investigate the differences between four distinct machine learning models, support vector machine (SVM), logistic regression, decision trees (DT), and artificial neural networks (ANN) in their classification accuracy and possible practicality in CVD classification. techniques such as ensemble learning and other model-specific optimizations are not part of this study, but more basic implementations of the various models were used. To implement abovementioned ML models, a subset of 14 features from the original heart disease dataset is considered and deemed relevant for classification where no individual feature data are missing. From the results, it is observed that there is no clear winner in the comparison of models. There is no significant difference in the average accuracy of models. The highest average hit rate is observed in SVM and ANN, however it is slightly lower in ANN. Even though the DT had lower accuracy, the fully trained model can be easily visualized and interpreted by humans. Hence, the DT is possibly the most practical model to use as a complement to doctors in their current methods of diagnosis.

关键词： average classification accuracy Cardiovascular Disease Computer Aided Diagnostics Machine Learning Algo-rithms Test Data Split

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Sensor Tracking System using Radar and Object Detection 13

Sensor Tracking System using Radar and Object Detection

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IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Melgoza, Cesar Martinez George, Kiran Miho, Jake Calif State Univ Fullerton Comp Engn Fullerton CA 92634 USA

ISBN: (纸本)9798350332865

Sensor technology has impacted and transformed several commercial and military industries, from automotive to defense, to personal security applications. Adopting Computer Vision techniques such as Object Detection helps boost the system's capabilities in tracking and classification. A previous study looked at optimizing a Radar System that relied on the processing power of the Raspberry Pi 4. Since the Raspberry Pi 4 is not an ideal device for a real-time environment, this experiment aims to improve on that aspect by utilizing the processing power of the Jetson TX2 to boost the system's speed and reduce latency between the Object Detection and Velocity calculation. The experiment will be repeated using the Jetson hardware to mimic the same scenarios in common tracking and surveillance systems.

关键词： Radar Transceiver HB100 Jetson TX2 Parallel Programming Computer Vision YOLOv3 Object Detection System Latency average classification accuracy

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Empirical study of neighbourhood rough sets based band selection techniques for classification of hyperspectral images

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IET IMAGE PROCESSING 2019年第8期13卷 1266-1279页

作者： Barman, Barnali Patra, Swarnajyoti Tezpur Univ Comp Sci & Engn Dept Tezpur 784028 Assam India

Hyperspectral images are acquired in hundreds of spectral channels that contain rich spectral information of different land-cover objects but at the same time they have high dimensionality with ample redundancy which rises curse of dimensionality and computational issues for classification. To mitigate such issues, band selection that reduces the dimensionality of hyperspectral data is a well-known approach widely used in the literature. Neighbourhood rough set, a variant of rough set capable of analysing continuous values, is a robust mathematical tool for handling uncertain and vague data. In this study, the authors have presented an empirical study of four forward greedy hyperspectral band selection algorithms implemented using the neighbourhood rough set, the variable precision neighbourhood rough set, the consistency measure of neighbourhood rough set and the granulation knowledge-based neighbourhood rough set. The effectiveness of these techniques is compared in terms of average classification accuracy, kappa accuracy and standard deviation obtained by using support vector machine classifier on three real hyperspectral data sets. From the experiments, it is found that the variable precision neighbourhood rough set and the consistency measure of neighbourhood rough set are more robust for selecting informative bands compared to the others. The effectiveness of these techniques is also validated by comparing with some state-of-the-art techniques.

关键词： image classification rough set theory data reduction remote sensing support vector machines geophysical image processing hyperspectral imaging empirical study forward greedy hyperspectral band selection algorithms variable precision neighbourhood rough set granulation knowledge-based neighbourhood rough set hyperspectral data sets hyperspectral images classification neighbourhood rough sets spectral channels rich spectral information hyperspectral data dimensionality reduction average classification accuracy

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EEG classification of Emotions Using Emotion-specific Brain Functional Network 37

EEG Classification of Emotions Using Emotion-specific Brain ...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Gonuguntla, V. Shafiq, G. Wang, Y. Veluvolu, K. C. Kyungpook Natl Univ Coll IT Engn Sch Elect Engn Daegu 702701 South Korea

ISBN: (纸本)9781424492701

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62% was obtained with the proposed technique.

关键词： electroencephalography medical signal processing signal classification synchronisation EEG classification average classification accuracy emotion-specific brain functional network emotion-specific dynamic network pattern human emotion reactive band reactive channel pairs subject-specific dynamic network pattern subject-specific functional network synchronization measure-phase-locking value synchrony variation Electrodes Electroencephalography Materials requirements planning Pragmatics Synchronization Time-frequency analysis Yttrium

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Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data

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BMC BIOINFORMATICS 2013年第Sup10期14卷 S2-S2页

作者： Kahara, Juhani Lahdesmaki, Harri Aalto Univ Sch Sci Dept Informat & Comp Sci FI-00076 Aalto Finland Turku Univ Turku Ctr Biotechnol Turku Finland

Transcription factor (TF) binding to DNA can be modeled in a number of different ways. It is highly debated which modeling methods are the best, how the models should be built and what can they be applied to. In this study a linear k-mer model proposed for predicting TF specificity in protein binding microarrays (PBM) is applied to a high-throughput SELEX data and the question of how to choose the most informative k-mers to the binding model is studied. We implemented the standard cross-validation scheme to reduce the number of k-mers in the model and observed that the number of k-mers can often be reduced significantly without a great negative effect on prediction accuracy. We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number. We compared prediction performance of k-mer and position specific weight matrix (PWM) models derived from the same SELEX data. Consistent with previous results on PBM data, performance of the k-mer model was on average 9%-units better. For the 15 proteins in the SELEX data set with medium enrichment cycles, classification accuracies were on average 71% and 62% for k-mer and PWMs, respectively. Finally, the k-mer model trained with SELEX data was evaluated on ChIP-seq data demonstrating substantial improvements for some proteins. For protein GATA1 the model can distinquish between true ChIP-seq peaks and negative peaks. For proteins RFX3 and NFATC1 the performance of the model was no better than chance.

关键词： Feature Selection classification accuracy average classification accuracy Feature Selection Strategy Affinity Score

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Performance of Electromyography Recorded Using Textile Electrodes in Classifying Arm Movements

Performance of Electromyography Recorded Using Textile Elect...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Li, Guanglin Geng, Yanjuan Tao, Dandan Zhou, Ping Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Hlth Informat Shenzhen 518055 Guangdong Peoples R China Rehabilitat Inst Chicago Chicago IL USA

ISBN: (纸本)9781424441228

Electromyography (EMG) signals are commonly recorded using the Ag/AgCl gel electrodes in myoelectric prosthetic control. While a gelled electrode may provide high-quality EMG recordings, it is inconvenient in clinical application of a myoelectric prosthesis. A novel type of signal sensors-textile electrodes should be ideal in control of myoelectric prostheses. However, it is unknown whether the performance of textile electrodes is comparable to commonly used electrodes in classifying arm movements. In this study, the custom-made bipolar textile electrodes were fabricated using copper-based nickel-plated conductive fabric and were used to record EMG signals. The performance of EMG signals recorded with textile electrodes in identifying nine arm and hand movements were investigated. Our pilot results showed that the average classification accuracy across six able-bodied subjects was 94.05% when using textile electrodes and 94.26% when using conventional electrodes, with no significant difference between the two types of electrodes (p=0.81). The pilot results suggest that the textile electrodes could achieve similar performance in classifying arm movements in control of myoelectric prostheses as the gelled metal electrodes.

关键词： accuracy Ag-AgCl EMG signals Electrodes Electromyography Metals Muscles Prosthetics Textiles able-bodied subjects arm movements average classification accuracy biomechanics biomedical electrodes copper-based nickel-plated conductive fabric custom-made bip

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Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis

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BIOMEDICAL ENGINEERING ONLINE 2011年第1期10卷 1-20页

作者： Turnip, Arjon Hong, Keum-Shik Jeong, Myung-Yung Pusan Natl Univ Dept Cogno Mechatron Engn Pusan 609735 South Korea Pusan Natl Univ Sch Mech Engn Pusan 609735 South Korea

Background: The electroencephalography (EEG) signals are known to involve the firings of neurons in the brain. The P300 wave is a high potential caused by an event-related stimulus. The detection of P300s included in the measured EEG signals is widely investigated. The difficulties in detecting them are that they are mixed with other signals generated over a large brain area and their amplitudes are very small due to the distance and resistivity differences in their transmittance. Methods: A novel real-time feature extraction method for detecting P300 waves by combining an adaptive nonlinear principal component analysis (ANPCA) and a multilayer neural network is proposed. The measured EEG signals are first filtered using a sixth-order band-pass filter with cut-off frequencies of 1 Hz and 12 Hz. The proposed ANPCA scheme consists of four steps: pre-separation, whitening, separation, and estimation. In the experiment, four different inter-stimulus intervals (ISIs) are utilized: 325 ms, 350 ms, 375 ms, and 400 ms. Results: The developed multi-stage principal component analysis method applied at the pre-separation step has reduced the external noises and artifacts significantly. The introduced adaptive law in the whitening step has made the subsequent algorithm in the separation step to converge fast. The separation performance index has varied from -20 dB to -33 dB due to randomness of source signals. The robustness of the ANPCA against background noises has been evaluated by comparing the separation performance indices of the ANPCA with four algorithms (NPCA, NSS-JD, JADE, and SOBI), in which the ANPCA algorithm demonstrated the shortest iteration time with performance index about 0.03. Upon this, it is asserted that the ANPCA algorithm successfully separates mixed source signals. Conclusions: The independent components produced from the observed data using the proposed method illustrated that the extracted signals were clearly the P300 components elicited by task-rel

关键词： P300 Component average classification accuracy Multilayer Neural Network Nonlinear Principal Component Analysis Whitened Signal

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Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms

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BMC BIOINFORMATICS 2010年第1期11卷 447-447页

作者： Guo, Yu Graber, Armin McBurney, Robert N. Balasubramanian, Raji Univ Massachusetts Dept Biostat & Epidemiol Amherst MA 01003 USA BG Med Inc Waltham MA 02451 USA Eduard Wallnoefer Zentrum 1 UMIT Inst Bioinformat & Translat Res A-6060 Hall In Tirol Austria Optimal Med Ltd Warwick CV35 9EF England

Background: Data generated using 'omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of signal-to-noise ratio in the dataset, imbalance in class distribution and choice of metric for quantifying performance of the classifier. To guide study design, we present a summary of the key characteristics of 'omics' data profiled in several human or animal model experiments utilizing high-content mass spectrometry and multiplexed immunoassay based techniques. Results: The analysis of data from seven 'omics' studies revealed that the average magnitude of effect size observed in human studies was markedly lower when compared to that in animal studies. The data measured in human studies were characterized by higher biological variation and the presence of outliers. The results from simulation studies indicated that the classifier Prediction Analysis for Microarrays (PAM) had the highest power when the class conditional feature distributions were Gaussian and outcome distributions were balanced. Random Forests was optimal when feature distributions were skewed and when class distributions were unbalanced. We provide a free open-source R statistical software library (MVpower) that implements the simulation strategy proposed in this paper. Conclusion: No single classifier had optimal performance under all settings. Simulation studies provide useful guidance for the design of biomedical studies involving high-di

关键词： Support Vector Machine Random Forest Simulated Dataset average classification accuracy Recursive Feature Elimination

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Study of stability of time-domain features for electromyographic pattern recognition

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2010年第1期7卷 21-21页

作者： Tkach, Dennis Huang, He Kuiken, Todd A. Rehabil Inst Chicago Neural Engn Ctr Artificial Limbs Chicago IL 60611 USA Univ Chicago Comm Computat Neurosci Chicago IL 60637 USA Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Northwestern Univ Dept Phys Med & Rehabil Chicago IL 60611 USA

Background: Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by (1) investigating the stability of time-domain EMG features during changes in the EMG signals and (2) identifying the feature sets that would provide the most robust EMG pattern recognition. Methods: Variations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study. Results: Muscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG feature set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features. Conclusions: Selecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent;however, this simple solution is still inadequate. Stabilizing electrode contact locations and developing effective classifier trainin

关键词： classification accuracy classification Performance Muscle Fatigue average classification accuracy Cepstrum Coefficient

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