Bicoherence Interpretation in EEG Requires Signal to Noise Ratio Quantification: An Application to Sensorimotor Rhythms

Tacchino, Giulia; Coelli, Stefania; Reali, Pierluigi; Galli, Manuela; Bianchi, Anna Maria

doi:10.1109/tbme.2020.2969278

Cited by 14 publications

(10 citation statements)

References 41 publications

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“…The estimated Bicoherence value reduces to 0.28 at SNR = -15dB and eventually diminishes to almost zero at SNR of -20dB. The results are similar to [39] where a different phase coupling model was used to report that Bicoherence fails to reliably detect phase coupling when SNR is less than -16 dB (or S/N = 0.158). As per their analysis [39, Fig.…”

Section: Fig 14 Experimental Setup To Study the Effect Of Vibration O...supporting

confidence: 74%

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

2022

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This paper presents a comprehensive evaluation of the near-field acoustics of an aircraft auxiliary power unit (APU), based on experimental data acquired from an in-situ APU. The aim is to establish whether near-field acoustics can be implemented for online condition monitoring. The APU of Cranfield University's demonstrator aircraft, a Boeing 737-400, has been instrumented to acquire acoustics (near-field and far-field) and vibration data in synchronization with aircraft state parameters under a wide range of operating conditions. The acquired data is first implemented to determine the efficacy of employing nearfield / far-field microphones, and vibration sensors, to monitor the combustion noise and tonal frequency levels from the APU components. Subsequently, an evaluation of the broadband characteristics of the vibroacoustic data and its variations against APU states and performance parameters is conducted based on several categories of feature extraction techniques. The findings suggest that nearfield acoustics lacks the ability to capture the combustion noise process. In addition, the tonal frequencies are also lost due to the level of background noise, fluctuations in the APU speeds, and scattering effects. For the same reasons, the phase couplings occurring between the signals generated by the APU components cannot be detected using acoustic data. Nevertheless, the overall analysis substantiates that the near-field acoustic data can be used to predict the APU operating states and has the potential to be implemented for developing APU performance parameter estimation models to enable condition monitoring.

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Section: Fig 14 Experimental Setup To Study the Effect Of Vibration O...supporting

confidence: 74%

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

2022

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“…A significant QPC enhancement (R BQPC ) with noise in brain circuits might offer a mechanism for higher-order coding bridging temporal and frequency coding schemes [85] [86] [87] [88] [89] [90]. A recent paper has emphasized QPC between the sensorimotor mu and beta components of EEG during the different phases of a motor task, even when mu and beta oscillations have a low SNR [91]. In addition, we suggest that the interpretation of QPC stochastic resonance should also be considered in the framework of deterministic chaotic dynamics with reentry circuits characterized by distinct frequencies of brain oscillations [92] [93].…”

Section: Discussionmentioning

confidence: 99%

Detection of phase coupling in interacting networks of cortical columns

Abé¹,

Asai²,

Villa³

2022

Preprint

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<div>This is a preprint version submitted to review for publication on the IEEE Transactions on Biomedical Engineering (TBME).</div><div><br></div><div>In this study we investigated the estimation method of cross-bicoherence to infer quadratic phase coupling (QPC) in interaction networks based on empirical Bayes estimation. Nonlinear interaction, ubiquitous in a wide range of biomedical fields, often makes it challenging to apply conventional parametric approaches. While cross-bicoherence has been recognized as a key statistic of QPC, prior studies have not elucidated how reliable the estimation can be for realistic time series. This work demonstrates that cross-bicoherence estimated by the proposed method effectively detects QPC, with illustrative simulation studies of both typical QPC and neural mass models of cortical columns.<br></div>

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“…A high-pass filter was applied at 0.1 Hz for the purpose of DC offset correction, while a notch filter was used at 60 Hz to eliminate the electrical interference. The raw signal was bandpass filtered between 7 and 32 Hz to exclude all of the frequency components other than mu and beta, as the studies [ 43 , 44 , 45 ] revealed the occurrence of MI patterns in the stated frequency range. However, the artifacts were removed using the EEGLAB-based artifact removal algorithm called artifact subspace reconstruction (ASR).…”

Section: Methodsmentioning

confidence: 99%

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Awais

Yusoff

Khan

et al. 2021

Sensors

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Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain’s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms—an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain–computer interfaces.

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Bicoherence Interpretation in EEG Requires Signal to Noise Ratio Quantification: An Application to Sensorimotor Rhythms

Cited by 14 publications

References 41 publications

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

Detection of phase coupling in interacting networks of cortical columns

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

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