Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

Ogino, Mikito; Kanoga, Suguru; Ito, Shin‐ichi; Mitsukura, Yasue

doi:10.1109/access.2021.3067337

Cited by 11 publications

(9 citation statements)

References 58 publications

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“…SWLDA can provide robust feature extraction and a good prediction for P300-based BCIs [ 44 ]. The most correlated features are selected to predict the targeted command based on statistical significance [ 45 ]. The conventional technique using N200 and P300 features and SWLDA can yield a high efficiency for both offline [ 31 , 32 , 33 ] and online testing [ 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

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Event-Related Potential-Based Brain–Computer Interface Using the Thai Vowels’ and Numerals’ Auditory Stimulus Pattern

Borirakarawin

Punsawad

2022

Sensors

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Herein, we developed an auditory stimulus pattern for an event-related potential (ERP)-based brain–computer interface (BCI) system to improve control and communication in quadriplegia with visual impairment. Auditory stimulus paradigms for multicommand electroencephalogram (EEG)-based BCIs and audio stimulus patterns were examined. With the proposed auditory stimulation, using the selected Thai vowel, similar to the English vowel, and Thai numeral sounds, as simple target recognition, we explored the ERPs’ response and classification efficiency from the suggested EEG channels. We also investigated the use of single and multi-loudspeakers for auditory stimuli. Four commands were created using the proposed paradigm. The experimental paradigm was designed to observe ERP responses and verify the proposed auditory stimulus pattern. The conventional classification method produced four commands using the proposed auditory stimulus pattern. The results established that the proposed auditory stimulation with 20 to 30 trials of stream stimuli could produce a prominent ERP response from Pz channels. The vowel stimuli could achieve higher accuracy than the proposed numeral stimuli for two auditory stimuli intervals (100 and 250 ms). Additionally, multi-loudspeaker patterns through vowel and numeral sound stimulation provided an accuracy greater than 85% of the average accuracy. Thus, the proposed auditory stimulation patterns can be implemented as a real-time BCI system to aid in the daily activities of quadratic patients with visual and tactile impairments. In future, practical use of the auditory ERP-based BCI system will be demonstrated and verified in an actual scenario.

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Section: Methodsmentioning

confidence: 99%

“…Thus, linear regression is used to include or exclude feature parameters for prediction. The test ERP dataset was computed with the feature weights to distinguish the target and determine a maximum score for classification [ 36 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

Event-Related Potential-Based Brain–Computer Interface Using the Thai Vowels’ and Numerals’ Auditory Stimulus Pattern

Borirakarawin

Punsawad

2022

Sensors

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“…To extract and forecast the obtained brain image features, CNN is introduced into the brain image feature extraction and diagnosis system. The proposed model can minimize the preprocessing workload and directly extract the most expressive features from the original data input without manual feature designation (Ogino et al, 2021). Figure 3 reduced, diminishing the over-fitting degree and the probability of local minimum.…”

Section: Performance Of S3vms In Brain Image Feature Extractionmentioning

confidence: 99%

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

et al. 2021

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The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.

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“…SSL techniques have previously been applied for biological signal-based applications [27][28][29], and BCI paradigms [30][31][32][33]. Ogino et al evaluated the advantages of a shrinkage linear discriminant analysis (SKLDA) with SSL for auditory BCI paradigms [34]. To this end, they attached pseudo labels predicted by classifiers for testing data and then updated the classifiers for each trial using the integrated pretraining and testing data.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous multiple-stimulus auditory brain–computer interface with semi-supervised learning and prior probability distribution tuning

Ogino¹,

Hamada²,

Mitsukura³

2022

J. Neural Eng.

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Objective. Auditory brain–computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However,existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli. Approach. For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR). Main results. The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ±11.46% and an ITR of 2.67 ± 1.09 bits/min, in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits/min, respectively. The ERP analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement. Significance. Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.

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Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

Cited by 11 publications

References 58 publications

Event-Related Potential-Based Brain–Computer Interface Using the Thai Vowels’ and Numerals’ Auditory Stimulus Pattern

Event-Related Potential-Based Brain–Computer Interface Using the Thai Vowels’ and Numerals’ Auditory Stimulus Pattern

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

Simultaneous multiple-stimulus auditory brain–computer interface with semi-supervised learning and prior probability distribution tuning

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