Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN

Du, Pu; Li, Penghai; Cheng, Longlong; Li, Xueqing; Su, Jianxian

doi:10.3389/fnins.2023.1132290

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…Because the datasets have imbalanced target and non-target ratios, accuracy cannot explain the result accurately. Many studies match the number of targets and non-targets by discarding the non-target trials or using other metrics to evaluate the imbalanced situation [35,36]. The primary approach is to use a confusion matrix that shows true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).…”

Section: Metricsmentioning

confidence: 99%

Signal alignment for cross-datasets in P300 brain-computer interfaces

Song,

Gwon,

Jun

et al. 2024

J. Neural Eng.

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Objective. Transfer learning has become an important issue in the brain-computer interface (BCI) field, and studies on subject-to-subject transfer within the same dataset have been performed. However, few studies have been performed on dataset-to-dataset transfer, including paradigm-to-paradigm transfer. In this study, we propose a signal alignment for P300 event-related potential (ERP) signals that is intuitive, simple, computationally less expensive, and can be used for cross-dataset transfer learning.Approach. We proposed a linear signal alignment that uses the P300’s latency, amplitude scale, and reverse factor to transform signals. For evaluation, four datasets were introduced (two from conventional P300 Speller BCIs, one from a P300 Speller with face stimuli, and the last from a standard auditory oddball paradigm).Results. Although the standard approach without signal alignment had an average precision score of 25.5%, the approach demonstrated a 35.8% average precision score, and we observed that the number of subjects showing improvement was 36.0% on average. Particularly, we confirmed that the Speller dataset with face stimuli was more comparable with other datasets. Significance. We proposed a simple and intuitive way to align ERP signals that uses the characteristics of ERP signals. The results demonstrated the feasibility of cross-dataset transfer learning even between datasets with different paradigms.

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Section: Metricsmentioning

confidence: 99%

Signal alignment for cross-datasets in P300 brain-computer interfaces

Song,

Gwon,

Jun

et al. 2024

J. Neural Eng.

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“…Lawhern et al [27] developed EEGNet, a compact CNN-based DNN that can analyze and classify brain signals from various mental activities. Additionally, there have been improved versions of EEGNet, such as the one by Zhang et al [28] for detecting single-trial P300 signals, as well as new DNN architectures like ST-CapsNet [29], which integrates spatial and temporal attentions using a capsule network for P300 detection, and a CNN-based approach by Du et al [30] that classifies single-trial P300 signals by fusing data from multiple subjects.…”

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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“…In EEG-BCIs, signals can be classified into evoked and spontaneous types. Evoked EEG involves triggering specific brain responses through external stimuli, such as P300 [20][21][22][23] and Steady-State Visual Evoked Potentials (SSVEPs) [24][25][26][27][28][29]. While extensively studied in BCI systems, P300 is susceptible to interference and prolonged fixation on light sources, while SSVEPs may lead to visual fatigue.…”

Section: Introductionmentioning

confidence: 99%

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

Huang,

Wang,

Fan

et al. 2024

Brain Sciences

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In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.

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Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN

Cited by 10 publications

References 35 publications

Signal alignment for cross-datasets in P300 brain-computer interfaces

Signal alignment for cross-datasets in P300 brain-computer interfaces

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

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

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