Search Results - (( developing eeg based algorithm ) OR ( java optimization path algorithm ))
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
Published 2021“…Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. …”
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SecPath: Energy efficient path reconstruction in wireless sensor network using iterative smoothing
Published 2019“…This work uses iterative smoothing algorithm to find an alternative path with less distance and energy consumption. …”
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4
Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…The training and parameters selection of the machine learning algorithms are conducted using EEG data collected from ten subjects in the laboratory. …”
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Multivariate EEG signal processing techniques for the aid of severely disabled people
Published 2022“…Therefore, the proposed research work is involved with three or more channels of EEG signal for online BCI. Two fundamental objectives for BCI based on motor movement imagery from multichannel signals are aimed at in this research work: i) to develop a technique of multivariate feature extraction for motor imagery related to multichannel EEG signals; and ii) to develop an appropriate machine learning based feature classification algorithm for Brain Computer Interface. …”
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Dingle's Model-based EEG Peak Detection using a Rule-based Classifier
Published 2015“…In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. The algorithm is developed into three stages: peak candidate detection, feature extraction, and classification. …”
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Energy efficient path reconstruction in wireless sensor network using iPath
Published 2019“…This work uses iterative boosting algorithm to find an alternative path with less distance and energy consumption. …”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…However, the developed algorithms only consider the selected features from a peak model based on the understanding of the EEG signals characteristics. …”
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EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN
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Visdom: Smart guide robot for visually impaired people
Published 2025“…Core functionalities such as path planning, autonomous movement, voice feedback, and app-to-robot communication have been thoroughly tested and optimized. …”
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EEG-based fatigue detection using binary pattern analysis and KNN algorithm
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EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
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EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
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Machine learning approach for stress detection based on alpha-beta and theta-beta ratios of EEG signals
Published 2021“…This work explores the impact of bandpower of alpha/beta and theta/beta ratios when combined with other features to classify two-levels of human stress based on EEG signals using five commonly used machine learning algorithms. …”
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Proceeding Paper -
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Recent Trends and Open Challenges in EEG based Brain-Computer Interface Systems
Published 2019“…In this paper, we have tried to mention some critical issues of EEG based BCI system including EEG modalities, EEG acquisition, signal processing algorithm and performance evaluation. …”
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Bio-signal identification using simple growing RBF-network (OLACA)
Published 2007“…The new algorithms are evaluated on a chaotic nonlinear biological based time series signals such as electroencephalographic (EEG) and electrocardiography (ECG). …”
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EEG-based brain source localization using visual stimuli
Published 2016“…These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. …”
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Bayesian Framework based Brain Source Localization Using High SNR EEG Data
Published 2019“…This research work discusses the results based on synthetically generated EEG data at an SNR level of 12 dB with Gaussian noise added linearly in data matrix. …”
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