Spatial-Temporal Neural Network for P300 Detection

Zhang, Zhen; Xin, Yu; Rong, Xianwei; Iwata, Makoto

doi:10.1109/access.2021.3132024

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…The classification performance is perhaps not high enough for efficient detection in practice. Other channel selection methods [39,52], machine learning algorithms, and deep learning neural networks, such as EEGNet [53], spatial-temporal neural networks [54], and other deep neural networks [31,32,55,56], can be explored to improve the recognition performance for practical applications.…”

Section: Discussionmentioning

confidence: 99%

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

Lian,

Qiao,

Zhao

et al. 2023

Brain Sciences

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Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 μV versus 7.84 ± 1.40 μV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain–computer interfaces in EEG-based target detection areas.

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Section: Discussionmentioning

confidence: 99%

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

Lian,

Qiao,

Zhao

et al. 2023

Brain Sciences

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“…Seven DNN models for EEG data (EEG DNNs) were tested in the preliminary inquiry for the training/validation dataset: (1) a custom DNN model including four conv1d layers, (2) a custom DNN model including four conv2d layers for band-passed EEG time series (delta, theta, alpha, beta, and gamma frequency band × time), (3) a recurrent neural network (RNN) model using long short term memory (LSTM), (4) a convolutional RNN (ConvRNN) model using LSTM after two conv1d layers, (5) a spatial-temporal neural network originally designed for P300 detection (Zhang et al, 2021), (6) a generic temporal convolutional network (TCN) (Bai et al ., 2018), and (7) a modified version of the TCN. Among them, we adopted the TCN model (6) because of its superior decoding performance and its simplicity (Figure 2D– E).…”

Section: Methodsmentioning

confidence: 99%

Multimodal deep neural decoding of visual object representation in humans

Watanabe

Miyoshi²,

Jimura

et al. 2022

Preprint

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Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. However, it is unclear how neural activities in spatially-distributed brain regions, especially in the aspect of temporal dynamics, represent visual objects. To address this issue, we explored spatial and temporal organization of visual object representations using concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), combined with neural decoding by deep neural networks (DNNs). Visualization of the fMRI DNN revealed that visual categorization (faces or non-face objects) occurred in brain-wide cortical regions, including the ventral temporal cortex. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of both DNNs improved classification performance for both categorization and sub-categorization. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner.

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“…For instance, Oralhan [25] introduced a three-dimensional convolutional neural network (3D-CNN) that achieves high accuracy in detecting P300. Zhang et al [26] proposed a DNN consisting of parallel spatial and temporal units for the same task. Lawhern et al [27] developed EEGNet, a compact CNN-based DNN that can analyze and classify brain signals from various mental activities.…”

Section: Introductionmentioning

confidence: 99%

Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering

Mobaien,

Boostani,

Sanei

2024

J. Neural Eng.

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Objective:The P300-based brain-computer interfaces (BCIs) typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience a elicitation of a P300 event-related potential in their electroencephalography (EEG). However, detecting the P300 signal can be challenging due to its very low signal-to-noise ratio (SNR), often compromised by the sequence of visual evoked potentials (VEPs) generated in the occipital regions of the brain in response to periodic visual stimuli. The main objective of this study is to investigate how visual evoked potentials (VEPs) impact P300-based BCIs. Subsequently, the study aims to propose a method for EEG spatial filtering to alleviate the effect of VEPs and enhance the overall performance of these BCIs.Approach:Our approach entails analyzing recorded EEG signals from visual P300-based BCIs through temporal, spectral, and spatial analysis techniques to identify the impact of VEPs. Subsequently, we introduce a regularized version of the xDAWN algorithm, a well-established spatial filter known for enhancing single-trial P300s. This aims to simultaneously enhance P300 signals and suppress VEPs, contributing to an improved overall signal quality.Main results:Analyzing EEG signals shows that VEPs can significantly contaminate P300 signals, resulting in a decrease in the overall performance of P300-based BCIs. However, our proposed method for simultaneous enhancement of P300 and suppression of VEPs has demonstrated improved performance in P300-based BCIs. This improvement has been verified through several experiments conducted with real P300 data.Significance:This study focuses on the effects of VEPs on the performance of P300-based BCIs, a problem that has not been adequately addressed in previous studies. It opens up a new path for investigating these BCIs. Moreover, the proposed spatial filtering technique has the potential to further enhance the performance of these systems.

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Spatial-Temporal Neural Network for P300 Detection

Cited by 10 publications

References 38 publications

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

Multimodal deep neural decoding of visual object representation in humans

Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering

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