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1.
BMC Bioinformatics ; 22(1): 26, 2021 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-33482716

RESUMO

BACKGROUND: Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal. RESULTS: The performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ([Formula: see text]) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was able to accurately decode the force values without explicitly using time lags in the input features. Additionally, the proposed method was able to predict zero-force values very accurately due to benefiting from an output nonlinearity. CONCLUSION: The proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.


Assuntos
Interfaces Cérebro-Computador , Córtex Motor , Redes Neurais de Computação , Animais , Análise dos Mínimos Quadrados , Movimento , Ratos
2.
Neural Netw ; 133: 193-206, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33220643

RESUMO

Motor imagery (MI) brain-computer interface (BCI) and neurofeedback (NF) with electroencephalogram (EEG) signals are commonly used for motor function improvement in healthy subjects and to restore neurological functions in stroke patients. Generally, in order to decrease noisy and redundant information in unrelated EEG channels, channel selection methods are used which provide feasible BCI and NF implementations with better performances. Our assumption is that there are causal interactions between the channels of EEG signal in MI tasks that are repeated in different trials of a BCI and NF experiment. Therefore, a novel method for EEG channel selection is proposed which is based on Granger causality (GC) analysis. Additionally, the machine-learning approach is used to cluster independent component analysis (ICA) components of the EEG signal into artifact and normal EEG clusters. After channel selection, using the common spatial pattern (CSP) and regularized CSP (RCSP), features are extracted and with the k-nearest neighbor (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, MI tasks are classified into left and right hand MI. The goal of this study is to achieve a method resulting in lower EEG channels with higher classification performance in MI-based BCI and NF by causal constraint. The proposed method based on GC, with only eight selected channels, results in 93.03% accuracy, 92.93% sensitivity, and 93.12% specificity, with RCSP feature extractor and best classifier for each subject, after being applied on Physionet MI dataset, which is increased by 3.95%, 3.73%, and 4.13%, in comparison with correlation-based channel selection method.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia/métodos , Imaginação/fisiologia , Movimento/fisiologia , Neurorretroalimentação/métodos , Neurorretroalimentação/fisiologia , Interfaces Cérebro-Computador/tendências , Causalidade , Análise Discriminante , Humanos , Máquina de Vetores de Suporte
3.
Medicine (Baltimore) ; 99(51): e22612, 2020 Dec 18.
Artigo em Inglês | MEDLINE | ID: mdl-33371056

RESUMO

BACKGROUND: Brain-computer interface-controlled functional electrical stimulation (BCI-FES) approaches as new feedback training is increasingly being investigated for its usefulness in improving the health of adults or partially impaired upper extremity function in individuals with stroke. OBJECTIVE: To evaluate the effects of BCI-FES on postural control and gait performance in individuals with chronic hemiparetic stroke. METHODS: A total of 25 individuals with chronic hemiparetic stroke (13 individuals received BCI-FES and 12 individuals received functional electrical stimulation [FES]). The BCI-FES group received BCI-FES on the tibialis anterior muscle on the more-affected side for 30 minutes per session, 3 times per week for 5 weeks. The FES group received FES using the same methodology for the same periods. This study used the Mann-Whitney test to compare the two groups before and after training. RESULTS: After training, gait velocity (mean value, 29.0 to 42.0 cm/s) (P = .002) and cadence (mean value, 65.2 to 78.9 steps/min) (P = .020) were significantly improved after BCI-FES training compared to those (mean value, 23.6 to 27.7 cm/s, and mean value, 59.4 to 65.5 steps/min, respectively) after FES approach. In the less-affected side, step length was significantly increased after BCI-FES (mean value, from 28.0 cm to 34.7 cm) more than that on FES approach (mean value, from 23.4 to 25.4 cm) (P = .031). CONCLUSION: The results of the BCI-FES training shows potential advantages on walking abilities in individuals with chronic hemiparetic stroke.


Assuntos
Interfaces Cérebro-Computador , Terapia por Estimulação Elétrica/métodos , Transtornos Neurológicos da Marcha/reabilitação , Reabilitação do Acidente Vascular Cerebral/métodos , Doença Crônica , Terapia por Estimulação Elétrica/instrumentação , Marcha/fisiologia , Transtornos Neurológicos da Marcha/etiologia , Humanos , Projetos Piloto , Equilíbrio Postural , Método Simples-Cego , Reabilitação do Acidente Vascular Cerebral/instrumentação , Velocidade de Caminhada
4.
BMC Neurol ; 20(1): 385, 2020 Oct 22.
Artigo em Inglês | MEDLINE | ID: mdl-33092554

RESUMO

BACKGROUND: Training with brain-computer interface (BCI) technology in the rehabilitation of patients after a stroke is rapidly developing. Numerous RCT investigated the effects of BCI training (BCIT) on recovery of motor and brain function in patients after stroke. METHODS: A systematic literature search was performed in Medline, IEEE Xplore Digital Library, Cochrane library, and Embase in July 2018 and was repeated in March 2019. RCT or controlled clinical trials that included BCIT for improving motor and brain recovery in patients after a stroke were identified. Data were meta-analysed using the random-effects model. Standardized mean difference (SMD) with 95% confidence (95%CI) and 95% prediction interval (95%PI) were calculated. A meta-regression was performed to evaluate the effects of covariates on the pooled effect-size. RESULTS: In total, 14 studies, including 362 patients after ischemic and hemorrhagic stroke (cortical, subcortical, 121 females; mean age 53.0+/- 5.8; mean time since stroke onset 15.7+/- 18.2 months) were included. Main motor recovery outcome measure used was the Fugl-Meyer Assessment. Quantitative analysis showed that a BCI training compared to conventional therapy alone in patients after stroke was effective with an SMD of 0.39 (95%CI: 0.17 to 0.62; 95%PI of 0.13 to 0.66) for motor function recovery of the upper extremity. An SMD of 0.41 (95%CI: - 0.29 to 1.12) for motor function recovery of the lower extremity was found. BCI training enhanced brain function recovery with an SMD of 1.11 (95%CI: 0.64 to 1.59; 95%PI ranging from 0.33 to 1.89). Covariates such as training duration, impairment level of the upper extremity, and the combination of both did not show significant effects on the overall pooled estimate. CONCLUSION: This meta-analysis showed evidence that BCI training added to conventional therapy may enhance motor functioning of the upper extremity and brain function recovery in patients after a stroke. We recommend a standardised evaluation of motor imagery ability of included patients and the assessment of brain function recovery should consider neuropsychological aspects (attention, concentration). Further influencing factors on motor recovery due to BCI technology might consider factors such as age, lesion type and location, quality of performance of motor imagery, or neuropsychological aspects. TRIAL REGISTRATION: PROSPERO registration: CRD42018105832 .


Assuntos
Interfaces Cérebro-Computador , Imaginação , Recuperação de Função Fisiológica , Reabilitação do Acidente Vascular Cerebral/métodos , Eletroencefalografia , Feminino , Humanos , Pessoa de Meia-Idade , Acidente Vascular Cerebral/fisiopatologia
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2869-2872, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018605

RESUMO

The goal of this paper is to investigate whether motor imagery tasks, performed under pain-free versus pain conditions, can be discriminated from electroencephalography (EEG) recordings. Four motor imagery classes of right hand, left hand, foot, and tongue are considered. A functional connectivity-based feature extraction approach along with a long short-term memory (LSTM) classifier are employed for classifying pain-free versus under-pain classes. Moreover, classification is performed in different frequency bands to study the significance of each band in differentiating motor imagery data associated with pain-free and under-pain states. When considering all frequency bands, the average classification accuracy is in the range of 77:86-80:04%. Our frequency-specific analysis shows that the gamma band results in a notably higher accuracy than other bands, indicating the importance of this band in discriminating pain/no-pain conditions during the execution of motor imagery tasks. In contrast, functional connectivity graphs extracted from delta and theta bands do not seem to provide discriminatory information between pain-free and under-pain conditions. This is the first study demonstrating that motor imagery tasks executed under pain and without pain conditions can be discriminated from EEG recordings. Our findings can provide new insights for developing effective brain computer interface-based assistive technologies for patients who are in real need of them.


Assuntos
Interfaces Cérebro-Computador , Imaginação , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Dor/diagnóstico
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2877-2880, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018607

RESUMO

Error-related potentials (ErrPs) can reflect the brain's response to errors. Recently, it has been used in the studies on neural mechanisms of human cognition, such as error detection and conflict monitoring. Moreover, ErrPs have provided technical support for the development of brain-computer interface (BCI). However, the different effects of visual stimulation modes (dynamic or static) on ErrPs have not been revealed. This may seriously affect the recognition accuracy of the ErrPs in practical applications. Therefore, the aim of this study was to investigate how people respond to different types of visual stimulations. Nineteen participants were recruited in the ErrPs-based tasks with two visual stimulation modes (dynamic and static). The ErrPs were analyzed and the feature values (N1, P2, P3, N6 and P8, named by the occurrence time) were statistically compared. The results showed that the difference between correctness and error was reflected in P3, N6, P8 in dynamic stimulation; and N1, P3, N6 and P8 in static stimulation. In the event-related potential based on error, the differences between dynamic and static tasks were reflected in N1 and P2. In conclusion, this study found that the features with later occurrence were significantly affected by correctness and error in both cases, while the error-related change in N1 only existed under the static stimulation. We also found that the recognition of stimulation modes came earlier within about 300 ms after the start of visual stimulation.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Encéfalo , Potenciais Evocados , Humanos , Estimulação Luminosa
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2946-2949, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018624

RESUMO

The steady-state visual evoked potential (SSVEP) is a robust brain activity that has been used in brain-computer interface (BCI) applications. However, previous studies of SSVEP-based BCIs give contradictory results on which stimulation medium provides the best performance. This paper describes a comparison of electroencephalography (EEG) decoding accuracy between using an LCD screen, clear LEDs, and frosted LEDs to deliver flashing light stimulation. The LCD screen and frosted LEDs achieved similar mean accuracies, and both of them were significantly better than clear LEDs. Background contrast with the LEDs did not significantly influence SSVEP decoding accuracy. A strong correlation was found between SSVEP accuracy and frequency domain magnitudes of EEG measurements.


Assuntos
Interfaces Cérebro-Computador , Potenciais Evocados Visuais , Eletroencefalografia , Exame Neurológico , Estimulação Luminosa
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2950-2953, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018625

RESUMO

Accurate and robust classification of Motor Imagery (MI) from Electroencephalography (EEG) signals is among the most challenging tasks in Brain-Computer Interface (BCI) field. To address this challenge, this paper proposes a novel, neuro-physiologically inspired convolutional neural network (CNN) named Filter-Bank Convolutional Network (FBCNet) for MI classification. Capturing neurophysiological signatures of MI, FBCNet first creates a multi-view representation of the data by bandpass-filtering the EEG into multiple frequency bands. Next, spatially discriminative patterns for each view are learned using a CNN layer. Finally, the temporal information is aggregated using a new variance layer and a fully connected layer classifies the resultant features into MI classes. We evaluate the performance of FBCNet on a publicly available dataset from Korea University for classification of left vs right hand MI in a subject-specific 10-fold cross-validation setting. Results show that FBCNet achieves more than 6.7% higher accuracy compared to other state-of-the-art deep learning architectures while requiring less than 1% of the learning parameters. We explain the higher classification accuracy achieved by FBCNet using feature visualization where we show the superiority of FBCNet in learning interpretable and highly generalizable discriminative features. We provide the source code of FBCNet for reproducibility of results.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Redes Neurais de Computação , Reprodutibilidade dos Testes , República da Coreia
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2963-2968, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018628

RESUMO

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Humanos , Aprendizagem , Aprendizado de Máquina , Redes Neurais de Computação
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2969-2972, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018629

RESUMO

Subject-independent brain-computer interfaces (SI-BCIs) which require no calibration process, are increasingly affect researchers in BCI field. The efficiencies (accuracies), however, were not satisfying till now. In this paper, we proposed a weighted subject-semi-independent classification method (WSSICM) for ERP based BCI system in which a few blocks data of target subject were used. 47 participants were attended in this study. We compared the accuracies of proposed method with traditional subject-specific classification method(SSCM) which used 15 blocks data of target subject. The averaged accuracies were 95.2% for the WSSICM at 5 blocks and 95.7% for the SSCM at 15 blocks. The accuracies of two method did not show significant difference (p-value=0.652). The method we proposed in this paper which could reduce the calibration time can be used for future BCI systems.


Assuntos
Interfaces Cérebro-Computador , Calibragem , Coleta de Dados , Humanos , Projetos de Pesquisa
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2973-2976, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018630

RESUMO

Event-related potential (ERP) speller can be utilized in device control and communication for locked-in or severely injured patients. However, problems such as inter-subject performance instability and ERP-illiteracy are still unresolved. Therefore, it is necessary to predict classification performance before performing an ERP speller in order to use it efficiently. In this study, we investigated the correlations with ERP speller performance using a resting-state before an ERP speller. In specific, we used spectral power and functional connectivity according to four brain regions and five frequency bands. As a result, the delta power in the frontal region and functional connectivity in the delta, alpha, gamma bands are significantly correlated with the ERP speller performance. Also, we predicted the ERP speller performance using EEG features in the resting-state. These findings may contribute to investigating the ERP-illiteracy and considering the appropriate alternatives for each user.


Assuntos
Interfaces Cérebro-Computador , Encéfalo , Eletroencefalografia , Potenciais Evocados , Lobo Frontal , Humanos
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2977-2980, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018631

RESUMO

A large amount of calibration data is typically needed to train an electroencephalogram (EEG)-based brain-computer interfaces (BCI) due to the non-stationary nature of EEG data. This paper proposes a novel weighted transfer learning algorithm using a dynamic time warping (DTW) based alignment method to alleviate this need by using data from other subjects. DTW-based alignment is first applied to reduce the temporal variations between a specific subject data and the transfer learning data from other subjects. Next, similarity is measured using Kullback Leibler divergence (KL) between the DTW aligned data and the specific subject data. The other subjects' data are then weighted based on their KL similarity to each trials of the specific subject data. This weighted data from other subjects are then used to train the BCI model of the specific subject. An experiment was performed on publicly available BCI Competition IV dataset 2a. The proposed algorithm yielded an average improvement of 9% compared to a subject-specific BCI model trained with 4 trials, and the results yielded an average improvement of 10% compared to naive transfer learning. Overall, the proposed DTW-aligned KL weighted transfer learning algorithm show promise to alleviate the need of large amount of calibration data by using only a short calibration data.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Aprendizado de Máquina
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2986-2990, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018633

RESUMO

Brain-computer interface (BCI) can provide a way for the disabled to interact with the outside world. Steady-state visual evoked potential (SSVEP), which evokes potential through visual stimulation is one of important BCI paradigms. In laboratory environment, the classification accuracy of SSVEPs is excellent. However, in motion state, the accuracy will be greatly affected and reduce quite a lot. In this paper, in order to improve the classification accuracy of the SSVEP signals in the motion state, we collected SSVEP data of five targets at three speeds of 0km/h, 2.5km/h and 5km/h. A compare network based on convolutional neural network (CNN) was proposed to learn the relationship between EEG signal and the template corresponding to each stimulus frequency and classify. Compared with traditional methods (i.e., CCA, FBCCA and SVM) and state-of-the-art method (CNN) on the collected SSVEP datasets of 20 subjects, the method we proposed always performed best at different speeds. Therefore, these results validated the effectiveness of the method. In addition, compared with the speed of 0 km / h, the accuracy of the compare network at a high walking rate (5km/h) did not decrease much, and it could still maintain a good performance.


Assuntos
Interfaces Cérebro-Computador , Caminhada , Eletroencefalografia , Potenciais Evocados Visuais , Humanos , Redes Neurais de Computação
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2991-2994, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018634

RESUMO

Electroencephalogram (EEG) data during motor imagery tasks regarding small-scale physical dynamics such as finger motions have low discriminability because capturing the spatial difference of the motions is difficult. We assumed that more discriminative features can be captured if spatial filters maximize the independence of each class data. This study constructed spatial filters named multiclass common spatial pattern (CSP), which maximize an approximation of mutual in-formation of extracted components and class labels, and applied them to a five-class motor-imagery dataset containing finger motion tasks. By applying multiclass CSP, the classification accuracies were improved (Mean SD: 40.6 ± 10.1%) compared with classical CSP (21.8 ± 2.5%) and no spatial filtering case (38.7±10.0%). In addition, we visualized learned spatial filters to assess the trend of discriminative features of finger motions. For these results, it was clear that multiclass CSP captured task-specific spatial maps for each finger motion and outperformed multiclass motor-imagery classification performance about 2% even when the tasks are small-scale physical dynamics.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Dedos , Imagens, Psicoterapia
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2995-2998, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018635

RESUMO

Brain-computer interfaces (BCIs) provide more independence to people with severe motor disabilities but current BCIs' performance is still not optimal and often the user's intentions are misinterpreted. Error-related potentials (ErrPs) are the neurophysiological signature of error processing and their detection can help improving a BCI's performance.A major inconvenience of BCIs is that they commonly require a long calibration period, before the user can receive feedback of their own brain signals. Here, we use the data of 15 participants and compare the performance of a personalized ErrP classifier with a generic ErrP classifier. We concluded that there was no significant difference in classification performance between the generic and the personalized classifiers (Wilcoxon signed rank tests, two-sided and one-sided left and right). This results indicate that the use of a generic ErrP classifier is a good strategy to remove the calibration period of a ErrP classifier, allowing participants to receive immediate feedback of the ErrP detections.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Encéfalo , Calibragem , Retroalimentação , Humanos
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3007-3010, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018638

RESUMO

Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer long short-term memory (LSTM)based artificial neural network (ANN) for decoding BMI neural signals. We collected motor cortical neural signals from a nonhuman primate (NHP), implanted with microelectrode array (MEA) while performing a directional joystick task. Next, we compared the LSTM model in decoding the joystick trajectories from the neural signals against the prevailing KF model. The results showed that the LSTM model yielded significantly improved decoding accuracy measured by mean correlation coefficient (0.84, p < 10-7) than the KF model (0.72). In addition, using a principal component analysis (PCA)-based dimensionality reduction technique yielded slightly deteriorated accuracies for both the LSTM (0.80) and KF (0.70) models, but greatly reduced the computational complexity. The results showed that the LSTM decoding model holds promise to improve decoding in BMIs for paralyzed humans.


Assuntos
Interfaces Cérebro-Computador , Redes Neurais de Computação , Animais , Humanos , Macaca mulatta , Microeletrodos , Movimento
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3011-3014, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018639

RESUMO

The estimation of the visual stimulus-based reaction time (RT) using subtle and complex information from the brain signals is still a challenge, as the behavioral response during perceptual decision making varies inordinately across trials. Several investigations have tried to formulate the estimation based on electroencephalogram (EEG) signals. However, these studies are subject-specific and limited to regression-based analysis. In this paper, for the first time to our knowledge, a generalized model is introduced to estimate RT using single-trial EEG features for a simple visual reaction task, considering both regression and classification-based approaches. With the regression-based approach, we could predict RT with a root mean square error of 111.2 ms and a correlation coefficient of 0.74. A binary and a 3-class classifier model were trained, based on the magnitude of RT, for the classification approach. Accuracy of 79% and 72% were achieved for the binary and the 3-class classification, respectively. Limiting our study to only high and low RT groups, the model classified the two groups with an accuracy of 95%. Relevant EEG channels were evaluated to localize the part of the brain significantly responsible for RT estimation, followed by the isolation of important features.Clinical relevance- Electroencephalogram (EEG) signals can be used in Brain-computer interfaces (BCIs), enabling people with neuromuscular disorders like brainstem stroke, amyotrophic lateral sclerosis, and spinal cord injury to communicate with assistive devices. However, advancements regarding EEG signal analysis and interpretation are far from adequate, and this study is a step forward.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Encéfalo , Humanos , Tempo de Reação , Análise de Regressão
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3015-3018, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018640

RESUMO

Electroencephalogram (EEG) based braincomputer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49(±8.74)% for motor execution (ME) and 40.36(±3.39)% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Força da Mão , Movimento (Física) , Movimento
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3019-3022, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018641

RESUMO

Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI) relies on overt spatial attention to exhibit reliable steady-state responses. There is a promising potential to employ the SSVEP paradigm in with vision research and clinical use, for instance, for visual field assessment. In this study, we investigate the SSVEP characteristics with different spatial attention, the different number of stimuli, and different viewing/visual angles. We collected data from eleven subjects in three experiment sessions, lasting about forty minutes, including the setup and calibration. Our evaluation results show similar SSVEP responses between overt and covert attention in multiple stimuli scenarios in most of the visual angles. We do not find any significant differences in SSVEP responses in visual angles between single and multi stimuli in covert attention. From this study, we found that reliable SSVEP responses can be achieved with covert spatial attention regardless of visual angles and stimulus spatial resolution.


Assuntos
Interfaces Cérebro-Computador , Potenciais Evocados Visuais , Atenção , Eletroencefalografia , Humanos , Campos Visuais
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