A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli

Zhang, Boyang; Zhou, Zongtan; Jiang, Jing

doi:10.3390/brainsci10080524

Cited by 9 publications

(4 citation statements)

References 69 publications

(76 reference statements)

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“…For patients who have impaired vision, BCIs based on visual stimulation are unsuitable. A recent study combining auditory and tactile stimulation to build a BCI speller achieved a much higher information transfer rate than using the auditory or tactile stimulation alone [51]. Another study built an electrotactile BCI based on SEPs [52].…”

Section: Applicationsmentioning

confidence: 99%

Tactile Location Perception Encoded by Gamma-Band Power

Chen,

Dong,

Gai

2024

Bioengineering

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Background: The perception of tactile-stimulation locations is an important function of the human somatosensory system during body movements and its interactions with the surroundings. Previous psychophysical and neurophysiological studies have focused on spatial location perception of the upper body. In this study, we recorded single-trial electroencephalography (EEG) responses evoked by four vibrotactile stimulators placed on the buttocks and thighs while the human subject was sitting in a chair with a cushion. Methods: Briefly, 14 human subjects were instructed to sit in a chair for a duration of 1 h or 1 h and 45 min. Two types of cushions were tested with each subject: a foam cushion and an air-cell-based cushion dedicated for wheelchair users to alleviate tissue stress. Vibrotactile stimulations were applied to the sitting interface at the beginning and end of the sitting period. Somatosensory-evoked potentials were obtained using a 32-channel EEG. An artificial neural net was used to predict the tactile locations based on the evoked EEG power. Results: We found that single-trial beta (13–30 Hz) and gamma (30–50 Hz) waves can best predict the tactor locations with an accuracy of up to 65%. Female subjects showed the highest performances, while males’ sensitivity tended to degrade after the sitting period. A three-way ANOVA analysis indicated that the air-cell cushion maintained location sensitivity better than the foam cushion. Conclusion: Our finding shows that tactile location information is encoded in EEG responses and provides insights on the fundamental mechanisms of the tactile system, as well as applications in brain–computer interfaces that rely on tactile stimulation.

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Section: Applicationsmentioning

confidence: 99%

Tactile Location Perception Encoded by Gamma-Band Power

Chen,

Dong,

Gai

2024

Bioengineering

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“…They obtain an accuracy of 85.2% in distinguishing the first two classes, which progressively decreases by adding the third and fourth classes, reaching 55.9%. In very recent work, Zhang, Zhou, and Jiang (2020) demonstrate that using a tactile and an auditory stimulus simultaneously allows increasing the implemented system's accuracy. In particular, using Bayesian linear discriminant analysis (BLDA), they manage to obtain on 12 subjects an average accuracy of 72.78% in making them recognize the desired cell in a 6 × 6 matrix.…”

Section: Related Workmentioning

confidence: 99%

Automatic stimuli classification from ERP data for augmented communication via Brain–Computer Interfaces

Leoni

Strada

Tanelli

et al. 2021

Expert Systems with Applications

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This work presents a supervised machine-learning approach to build an expert system that provides support to the neuroscientist in automatically classifying ERP data and matching them with a multisensorial alphabet of stimuli. To do this, two different approaches are considered: a hierarchical tree-based algorithm, XGBoost, and feedfoward neural networks, highlighting the pros and cons of both approaches in the different steps of the classification task. Moreover, the sensitivity of the classification capabilities of the tool as a function of the number of available electrodes is also studied, highlighting what can be achieved by applying the method using commercial, wearable EEG systems. The main novelty of this work consists in significantly enlarging the pool of stimuli that the expert system can recognize and comprising different, possibly mixed, sensorial domains. The obtained results open the way to the design of portable devices for augmented communication systems, which can be of particular interest for the development of advanced Brain-Computer Interfaces (BCI) for communication with different types of neurologically impaired patients.

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“…Event-related potentials (ERPs) are also key to BCI applications. Zhang et al developed a novel 36class bimodal ERP-BCI system based on tactile and auditory stimuli [13], and Wu et al studied the effect of noise on ERP-BCI in a real environment to establish a generalized method for BCI [14]. Researchers have combined multiple control schemes to establish a hybrid BCI.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Level Separation: A Method for Enhancing Electroencephalogram Classification Accuracy Through Super-Resolution Level Separation

Sun,

Li,

Zhang

2024

IEEE Access

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Accurate identification of electroencephalogram (EEG) signals forms the basis for the development and application of brain-computer interface (BCI) devices. Signal preprocessing is an essential part of most EEG classification and recognition systems.In this study, we proposed a method called Superresolution level separation (SRLS) to add dimensions of information to the classification model. First, we calculated the correlation between the EEG signals acquired by each channel and divided these channels into multiple levels. Next, we used a super-resolution method to calculate common EEGs (C-EEGs) acquired by the channels. In addition, we designed an EEG-split-informer (ES-informer) model based on an informer model to enable small-sample users to obtain highly fitting C-EEGs. We then calculated the difference between the C-EEG and true EEG (T-EEG) to obtain the unique EEGs (U-EEGs) acquired by each channel, thus, adding dimensions to the data inputted to the classification model. Utilizing the 2008 2a motor imagery (MI) EEG dataset from the BCI Competition and the P300 paradigm data collected, EEGNet was employed as the classification model to validate the efficacy of the proposed SRLS method. The results of the experiments indicated that the SRLS method augmented the input dimension of the model and amplified the classification accuracy by over 7% for MI and 3.6% for P300. These findings demonstrate that SRLS is capable of enhancing the recognition accuracy of EEG.

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A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli

Cited by 9 publications

References 69 publications

Tactile Location Perception Encoded by Gamma-Band Power

Tactile Location Perception Encoded by Gamma-Band Power

Automatic stimuli classification from ERP data for augmented communication via Brain–Computer Interfaces

Super-Resolution Level Separation: A Method for Enhancing Electroencephalogram Classification Accuracy Through Super-Resolution Level Separation

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