Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

Sharma, Lakhan Dev; Chhabra, Himanshu; Chauhan, Urvashi; Saraswat, Ritesh Kumar; Sunkaria, Ramesh Kumar

doi:10.1007/s41870-021-00807-7

Cited by 23 publications

(10 citation statements)

References 29 publications

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“…The Hilbert transform, often known as the HT, is an essential part of the HVD approach, as the name of this technique suggests. For the purpose of signal decomposition of EEG data, Sharma et al [ 62 , 63 ] employed the sliding window transform (SWT) and sliding mode-SSA (SM-SSA) [ 64 ]. In [ 65 ], Multivariant variational mode decomposition (MVMD) and PCA were used for EOG artifact elimination and attained the CRC value of 0.632.…”

Section: Discussionmentioning

confidence: 99%

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

Yedukondalu

Sharma

2023

Sensors

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Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

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Section: Discussionmentioning

confidence: 99%

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

Yedukondalu

Sharma

2023

Sensors

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“…These included the Gaussian Naive Bayes (GNB) classifier [29], noted for its simplicity and computational efficiency when dealing with high-dimensional data distributions. The KNN algorithm [30] [31], adept at discerning intricate patterns, was also utilized. Additionally, we leveraged the ensemble learning capabilities of RF model [32] [31] [33], well-suited for handling large, high-dimensional datasets.…”

Section: A Initial Model Trainingmentioning

confidence: 99%

MINDSCOPE: Machine-learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation

Katinni,

Bablani

2024

Preprint

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Passive brain-computer interfaces (P-BCIs) offer the ability to dynamically adapt system interactions by inferring users’ cognitive states, such as levels of mental workload, from neural data. The P-BCI devices have many applications from analyzing and designing educational materials to monitoring the mental health of professionals operating airplanes, nuclear power plants, and airline traffic dispatchers. Electroencephalography (EEG) is the most suitable P-BCI device that can easily find applications in daily life due to its cost efficiency and device setup simplicity as compared to other devices like Functional near- infrared spectroscopy (fNIRS) or Functional Magnetic Resonance Imaging (fMRI) that are either costly or are bulky equipment that cannot be used in real life scenarios. The goal of this work is to classify the cognitive load of the user using his/her EEG output using machine learning (ML) techniques, following the MINDSCOPE (Machine Learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation) methodology. The data acquisition was done by recording the EEG signal of the users performing n-back tasks of two different load intensities (rest and 2-back). This study accurately classified user cognitive load as high or low, allowing for comparison of several ML models and their performance in cognitive load discrimination. Feature subset analysis identified the combina- tion with the highest classification accuracy, shedding light on effective feature characterization for cognitive load assessment. Depending on the context of P-BCI use, the current work gives a starting point for choosing appropriate ML paradigms and feature characterizations to incorporate for a specific cognitive assessment task.

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“…The goal of Bayesian optimization method is to find the smallest scalar target function f (x) in a domain of x. To achieve the optimal tuning parameters and decreasing e error bound of the KNN, Bayesian optimization method is applied [18]. According to Figure 2…”

Section: Improved Bayesian Optimizable K-nearest Neighbor (Boknn) Alg...mentioning

confidence: 99%

Enhanced K-NN with Bayesian Optimization Algorithm for Predicting Energy Efficiency of Smart Grids in IoT

ZHAO,

Yuanchao,

Xiaopeng

et al. 2023

Preprint

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With the increasing number of end users using electricity in modern cities, smart grids have some critical problems for energy efficiency and managing renewable energy resources. Therefore, electricity load forecasting is an important strategy to avoid power disconnection and power communication damages in smart grids. On the other hand, the Internet of Things (IoT) devices can collect appropriate data from end users with smart metering. In addition, smart devices can check and gather critical data from power stations using smart sensors and actuators to provide the Quality of Service (QoS) factors and energy efficiency of electricity transmission in the smart girds. Due to the huge data transmission, machine learning is useful for evaluating electricity load forecasting and power stability in smart grids. For detecting electric load forecasting and energy consumption factors, this paper develops Bayesian Optimization for K-nearest neighbor (BOKNN) with the hyper-parameters function to detect the electricity load forecasting and estimated power consumption factors in smart grids. The proposed technique optimizes the performance of electricity load forecasting and enhances the structure of the machine learning efficiency with high model accuracy. Two real-data sets as our case studies for Short-Term Electricity Load Forecasting are applied to evaluate the proposed BOKNN as existing experimental testbeds. The simulation results illustrate that the proposed BOKNN provides optimized high accuracy with 88.33% and 98.13% correlation coefficient and minimum Mean Absolute Error (MAE) with 0.04% for existing datasets. The experimental results show that the suggested BOKNN model greatly outperforms the state-of-the-art prediction algorithms.

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Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

Cited by 23 publications

References 29 publications

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

MINDSCOPE: Machine-learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation

Enhanced K-NN with Bayesian Optimization Algorithm for Predicting Energy Efficiency of Smart Grids in IoT

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