BHCR: RSVP target retrieval BCI framework coupling with CNN by a Bayesian method

Huang, Liangtao; Zhao, Yi; Zeng, Ying; Lin, Zhiping

doi:10.1016/j.neucom.2017.01.061

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…Electroencephalography (EEG) is a widely used physiological technique that is easy to operate and economical, which aims to measure the electrical activity of the neurons in the brain propagated at the scalp level [9]. Thus EEG analysis has been extensively used in diverse application areas such as epilepsy seizure onset prediction [22], braincomputer interface (BCI) [23][24][25], emotion recognition [26,27], driver distraction [28], mental workload measurement [29], and many other neurological disorder diagnoses [30,31]. However, EEG is a multidimensional, non-stationary signal with poor signal-to-noise ratio characteristics [32].…”

Section: Related Workmentioning

confidence: 99%

“…Noise is often spread across entire frequency bands, and the noise amplitude is negligible compared to the amplitude of the actual signal itself, thus more identifiable [2,32]. Additionally, in the frequency domain, EEG data can be analyzed using an amplitude spectrum that separates signals into frequency bands such as delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30-45 Hz) bands, usually employing Fourier transformation [34]. Eventually, in the spatial domain, the locations of the electrodes can also give important information, thus enriching the EEG data analysis [34,35].…”

Section: Related Workmentioning

confidence: 99%

“…The next step in the pre-processing pipeline was to transform the EEG signals into the frequency domain by employing the Fast Fourier Transform (FFT) (2 of Figure 1). The transformed data in the frequency domain were separated into the five EEG bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-45 Hz) [34].…”

Section: Data Pre-processingmentioning

confidence: 99%

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On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

Chikkankod

Longo

2022

MAKE

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Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis. Techniques such as Independent Component Analysis (ICA) require human intervention to remove noise and artifacts. Autoencoders have automatized artifact detection and removal by representing inputs in a lower dimensional latent space. However, little research is devoted to understanding the minimum dimension of such latent space that allows meaningful input reconstruction. Person-specific convolutional autoencoders are designed by manipulating the size of their latent space. A sliding window technique with overlapping is employed to segment varied-sized windows. Five topographic head-maps are formed in the frequency domain for each window. The latent space of autoencoders is assessed using the input reconstruction capacity and classification utility. Findings indicate that the minimal latent space dimension is 25% of the size of the topographic maps for achieving maximum reconstruction capacity and maximizing classification accuracy, which is achieved with a window length of at least 1 s and a shift of 125 ms, using the 128 Hz sampling rate. This research contributes to the body of knowledge with an architectural pipeline for eliminating redundant EEG data while preserving relevant features with deep autoencoders.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Data Pre-processingmentioning

confidence: 99%

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On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

Chikkankod

Longo

2022

MAKE

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“…In the applications that benefit from this paradigm, computers are unable to analyze and understand images with deep semantic and unstructured features as successfully as humans, and the manual analysis tools are slow, which makes the study of RSVP-BCI more and more popular in recent decades. RSVP-BCI has been used in counterintelligence, police, and health care that require professionals to review objects, scenes, people, and other relevant information contained in a large number of images ( Huang et al, 2017 ; Singh and Jotheeswaran, 2018 ; Wu et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

Zhang

Wang

Zhang³

et al. 2020

Front. Neurosci.

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This paper reports on a benchmark dataset acquired with a brain–computer interface (BCI) system based on the rapid serial visual presentation (RSVP) paradigm. The dataset consists of 64-channel electroencephalogram (EEG) data from 64 healthy subjects (sub1,…, sub64) while they performed a target image detection task. For each subject, the data contained two groups (“A” and “B”). Each group contained two blocks, and each block included 40 trials that corresponded to 40 stimulus sequences. Each sequence contained 100 images presented at 10 Hz (10 images per second). The stimulus images were street-view images of two categories: target images with human and non-target images without human. Target images were presented randomly in the stimulus sequence with a probability of 1∼4%. During the stimulus presentation, subjects were asked to search for the target images and ignore the non-target images in a subjective manner. To keep all original information, the dataset was the raw continuous data without any processing. On one hand, the dataset can be used as a benchmark dataset to compare the algorithms for target identification in RSVP-based BCIs. On the other hand, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data through offline simulation. Furthermore, the dataset also provides high-quality data for characterizing and modeling event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEPs) in RSVP-based BCIs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html .

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“…The result was a principled way of dealing with outliers in simple data that was, yet achieved the AUC value of 0.75 in cross-subject BCI ( Kadioglu et al, 2018 ). As for the pattern recognition, some algorithms ( Gordon et al, 2017 ; Hajinoroozi et al, 2017 ; García-Salinas et al, 2019 ; Fernandez-Rodriguez et al, 2020 ), such as genetic algorithm and transfer learning ( Huang et al, 2011 , 2017 ; Jalilpour et al, 2020 ), were applied to improve the accuracy and speed of the spellers. On the other hand, the best AUC performance was still not higher than 0.75 ( Krusienski et al, 2006 ; Koçanaoğulları et al, 2020 ), and most works were still based on small samples.…”

Section: Introductionmentioning

confidence: 99%

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

Liu

Wang

Sheng

et al. 2020

Front. Hum. Neurosci.

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The brain-computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range of applications. However, the cross-subject recognition in BCI systems is difficult to break apart from the individual specific characteristics, unsteady characteristics, and environmental specific characteristics, which also makes it difficult to develop highly reliable and highly stable BCI systems. Rapid serial visual presentation (RSVP) is one of the most recent spellers with a clean, unified background and a single stimulus, which may evoke event-related potential (ERP) patterns with less individual difference. In order to build a BCI system that allows new users to use it directly without calibration or with less calibration time, RSVP was employed as evoked paradigm, then correlation analysis rank (CAR) algorithm was proposed to improve the cross-individual classification and simultaneously use as less training data as possible. Fifty-eight subjects took part in the experiments. The flash stimulation time is 200 ms, and the off time is 100 ms. The P300 component was locked to the target representation by time. The results showed that RSVP could evoke more similar ERP patterns among subjects compared with matrix paradigm. Then, the included angle cosine was calculated and counted for averaged ERP waveform between each two subjects. The average matching number of all subjects was 6 for the matrix paradigm, while for the RSVP paradigm, the average matching number range was 20 when the threshold value was set to 0.5, more than three times as much larger, quantificationally indicating that ERP waveforms evoked by the RSVP paradigm produced smaller individual differences, and it is more favorable for crosssubject classification. Information transfer rates (ITR) were also calculated for RSVP and matrix paradigms, and the RSVP paradigm got the average ITR of 43.18 bits/min, which was 13% higher than the matrix paradigm. Then, the receiver operating characteristic (ROC) curve value was computed and compared using the proposed CAR algorithm and traditional random selection. The results showed that the proposed CAR got significantly better performance than the traditional random selection and got the best AUC value

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BHCR: RSVP target retrieval BCI framework coupling with CNN by a Bayesian method

Cited by 11 publications

References 28 publications

On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

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