Deep Convolutional Neural Network Architectures for Tonal Frequency Identification in a Lofargram

Park, Jihun; Jung, Dae-Jin

doi:10.1007/s12555-019-1014-4

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…As machine learning (ML) has rapidly become a state-of-the-art analysis tool, researchers have considered searching for classification features (Park and Jung, 2021 ). The qualitative aspects of these RPs can be used for classification.…”

Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

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Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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“…Deep learning algorithms have been developed to increase classification accuracy and stability (Shan et al, 2019 ; Park and Jung, 2020 ; Sung et al, 2020 ). RNNs have been developed to improve their performance likewise; memristor-based RNNs (Yang et al, 2021 ), chaotic delayed RNNs with unknown parameters and stochastic noise (Yan et al, 2019 ), reformed recurrent Hermite polynomial neural network (Lin and Ting, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

Yoo

Santosa

Kim

2021

Front. Hum. Neurosci.

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This study aims to decode the hemodynamic responses (HRs) evoked by multiple sound-categories using functional near-infrared spectroscopy (fNIRS). The six different sounds were given as stimuli (English, non-English, annoying, nature, music, and gunshot). The oxy-hemoglobin (HbO) concentration changes are measured in both hemispheres of the auditory cortex while 18 healthy subjects listen to 10-s blocks of six sound-categories. Long short-term memory (LSTM) networks were used as a classifier. The classification accuracy was 20.38 ± 4.63% with six class classification. Though LSTM networks’ performance was a little higher than chance levels, it is noteworthy that we could classify the data subject-wise without feature selections.

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“…Target detection using acoustic measurements is important for identifying and tracking underwater target signals [ 1 , 2 , 3 ]. Techniques, such as energy detection (ED) [ 4 , 5 , 6 ], constant false alarm rate (CFAR) [ 7 , 8 , 9 ], machine learning (ML) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], and compressive sensing (CS) [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], have been proposed for efficient target detection.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has been applied to beamforming, classification, depth estimation, and target detection [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] in underwater acoustics. Even though ML-based schemes have achieved great scientific results, their application is limited because sufficient training data and hyperparameters that users need to tune and optimize are required [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning

Shin

Hong

Lee

et al. 2022

Sensors

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Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results.

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Deep Convolutional Neural Network Architectures for Tonal Frequency Identification in a Lofargram

Cited by 15 publications

References 19 publications

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning

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