A review of feature extraction and classification algorithms for image RSVP-based BCI

Wang, Zhengwei; Healy, Graham; Smeaton, Alan F.; Ward, Tomás E.

doi:10.1049/pbce114e_ch12

Cited by 9 publications

(5 citation statements)

References 56 publications

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“…In practice, when authors propose a given BCI system, they also describe the implemented encoding that comprises a set of algorithms for classification. Some common classifiers for P300, according to Wang et al ( 2018 ) and Abiri et al ( 2019 ) are Linear Discriminant Analysis, Bayesian Regression Analysis, Stepwise Discriminant Analysis, Support Vector Machines, and Artificial Neural Networks, although, in recent years, there has also been an important interest in Deep Learning based classifiers (Aggarwal and Chugh, 2022 ). In any case, whichever the classifier is, the parameters to reproduce findings and results are completely required.…”

Section: Methodsmentioning

confidence: 99%

Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

Bianchi¹,

Ferrante²,

Hu³

et al. 2022

Front. Neuroergonomics

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BackgroundIn the last decades, the P300 Speller paradigm was replicated in many experiments, and collected data were released to the public domain to allow research groups, particularly those in the field of machine learning, to test and improve their algorithms for higher performances of brain-computer interface (BCI) systems. Training data is needed to learn the identification of brain activity. The more training data are available, the better the algorithms will perform. The availability of larger datasets is highly desirable, eventually obtained by merging datasets from different repositories. The main obstacle to such merging is that all public datasets are released in various file formats because no standard way is established to share these data. Additionally, all datasets necessitate reading documents or scientific papers to retrieve relevant information, which prevents automating the processing. In this study, we thus adopted a unique file format to demonstrate the importance of having a standard and to propose which information should be stored and why.MethodsWe described our process to convert a dozen of P300 Speller datasets and reported the main encountered problems while converting them into the same file format. All the datasets are characterized by the same 6 × 6 matrix of alphanumeric symbols (characters and numbers or symbols) and by the same subset of acquired signals (8 EEG sensors at the same recording sites).Results and discussionNearly a million stimuli were converted, relative to about 7000 spelled characters and belonging to 127 subjects. The converted stimuli represent the most extensively available platform for training and testing new algorithms on the specific paradigm – the P300 Speller. The platform could potentially allow exploring transfer learning procedures to reduce or eliminate the time needed for training a classifier to improve the performance and accuracy of such BCI systems.

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Section: Methodsmentioning

confidence: 99%

Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

Bianchi¹,

Ferrante²,

Hu³

et al. 2022

Front. Neuroergonomics

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“…By presenting the experimental participants with an image sequence at a rate ranging from 2 to 20 Hz, it is possible to decode the target image on which the subjects are focusing from a massive pool of images using their electroencephalogram (EEG) patterns. Because this is an oddball event, the probability of the target image appearing is typically approximately 5-10% [2,3]. Initially used for psychological tests, such as short-term memory and attentional blinking, RSVP has been applied as a tool for target detection in various fields, including military target detection, topographic surveys, and medical image recognition.…”

Section: Introductionmentioning

confidence: 99%

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface

Zhang,

Xu,

Zhang

et al. 2024

Bioengineering

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The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.

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“…RSVP generates such event-related potentials (ERPs) and invokes a P300 component which is a large positive potential that reaches its peak with a latency of 300 ms after the stimulus onset [21,22]. To extract these potential variations to differentiate target/non-target conditions, robust signal processing and machine learning (ML) techniques are extremely necessary [19,[23][24][25][26]. SSVEP and RSVP are some of the most popular techniques used in EEG-based BCI systems [27,28].…”

Section: Introductionmentioning

confidence: 99%

SSVEP-assisted RSVP brain–computer interface paradigm for multi-target classification

Sankar

Huang

et al. 2021

J. Neural Eng.

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Brain–computer Interface (BCI) is actively involved in optimizing the communication medium between the human brain and external devices.Objective. Rapid serial visual presentation (RSVP) is a robust and highly efficient BCI technique in recognizing target objects but suffers from limited target selections. Hybrid BCI systems that combine steady-state visual evoked potential (SSVEP) and RSVP can mitigate this limitation and allow users to operate on multiple targets. Approach. This study proposes a novel hybrid SSVEP-RSVP BCI to improve the performance of classifying the target/non-target objects in a multi-target scenario. In this paradigm, SSVEP stimulation helps in identifying the user’s focus location and RSVP stimuli that elicit event-related potentials differentiate target and non-target objects. Main results. The proposed model achieved an offline accuracy of 81.59% by using 12 electroencephalography (EEG) channels and an online (real-time) accuracy of 78.10% when only four EEG channels are considered. Further, the biomarkers of physiological states are analyzed to assess the cognitive states (mental fatigue and user attention) of the participants based on resting theta and alpha band powers. The results indicate an inverse relationship between the BCI performance and the resting EEG power, validating that the subjects’ performance is affected by physiological states for long-term use of the BCI. Significance. Our findings demonstrate that the combination of SSVEP and RSVP stimuli improves the BCI performance and further enhances the possibility of performing multiple user command tasks, which are inevitable in real-world applications. Additionally, the cognitive state biomarkers discussed imply the need for an efficient and attractive experimental paradigm that reduces the physiological state disparities and provide enhanced BCI performance.

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A review of feature extraction and classification algorithms for image RSVP-based BCI

Cited by 9 publications

References 56 publications

Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface

SSVEP-assisted RSVP brain–computer interface paradigm for multi-target classification

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