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1.
Artigo em Inglês | MEDLINE | ID: mdl-33657004

RESUMO

Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.

2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 422-425, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018018

RESUMO

Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological status assessment is the problem of transfer learning caused by the domain inconsistency of biosignals across users or different recording sessions from the same user. We propose an adversarial inference approach for transfer learning to extract disentangled nuisance-robust representations from physiological biosignal data in stress status level assessment. We exploit the trade-off between task-related features and person-discriminative information by using both an adversary network and a nuisance network to jointly manipulate and disentangle the learned latent representations by the encoder, which are then input to a discriminative classifier. Results on cross-subjects transfer evaluations demonstrate the benefits of the proposed adversarial framework, and thus show its capabilities to adapt to a broader range of subjects. Finally we highlight that our proposed adversarial transfer learning approach is also applicable to other deep feature learning frameworks.


Assuntos
Aprendizado Profundo , Aprendizagem , Aprendizado de Máquina , Informações Pessoalmente Identificáveis , Registros
3.
IEEE Trans Biomed Eng ; 67(1): 69-78, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-30932828

RESUMO

OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking-based feature selection by any criterion, we propose to extend this focus with an information theoretic learning-driven feature transformation concept. METHODS: We present a maximum mutual information linear transformation and a nonlinear transformation framework derived by a general definition of the feature transformation learning problem. Empirical assessments are performed based on electroencephalographic data recorded during a four class motor imagery brain-computer interface (BCI) task. Exploiting the state-of-the-art methods for initial feature vector construction, we compare the proposed approaches with conventional feature selection-based dimensionality reduction techniques, which are widely used in brain interfaces. Furthermore, for the multi-class problem, we present and exploit a hierarchical graphical model-based BCI decoding system. RESULTS: Both binary and multi-class decoding analyses demonstrate significantly better performances with the proposed methods. CONCLUSION: Information theoretic feature transformations are capable of tackling potential confounders of conventional approaches in various settings. SIGNIFICANCE: We argue that this concept provides significant insights to extend the focus on feature selection heuristics to a broader definition of feature transformation learning in brain interfaces.

4.
IEEE Signal Process Lett ; 26(5): 710-714, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-31814690

RESUMO

Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.

5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2018: 5745-5748, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30441641

RESUMO

It has been suggested that changes in physiological arousal precede potentially dangerous aggressive behavior in youth with autism spectrum disorder (ASD) who are minimally verbal (MV-ASD). The current work tests this hypothesis through time-series analyses on biosignals acquired prior to proximal aggression onset. We implement ridge-regularized logistic regression models on physiological biosensor data wirelessly recorded from 15 MV-ASD youth over 64 independent naturalistic observations in a hospital inpatient unit. Our results demonstrate proof-of-concept, feasibility, and incipient validity predicting aggression onset 1 minute before it occurs using global, person-dependent, and hybrid classifier models.


Assuntos
Agressão , Transtorno do Espectro Autista/diagnóstico , Técnicas Biossensoriais , Adolescente , Humanos , Pacientes Internados
6.
Artigo em Inglês | MEDLINE | ID: mdl-30420938

RESUMO

We test the hypothesis that changes in preceding physiological arousal can be used to predict imminent aggression proximally before it occurs in youth with autism spectrum disorder (ASD) who are minimally verbal (MV-ASD). We evaluate this hypothesis through statistical analyses performed on physiological biosensor data wirelessly recorded from 20 MV-ASD youth over 69 independent naturalistic observations in a hospital inpatient unit. Using ridge-regularized logistic regression, results demonstrate that, on average, our models are able to predict the onset of aggression 1 minute before it occurs using 3 minutes of prior data with a 0.71 AUC for global, and a 0.84 AUC for person-dependent models.

7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2018: 1964-1967, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30440783

RESUMO

We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.


Assuntos
Conscientização , Interfaces Cérebro-Computador , Eletroencefalografia , Gestos , Robótica
8.
J Neural Eng ; 14(4): 046027, 2017 08.
Artigo em Inglês | MEDLINE | ID: mdl-28367834

RESUMO

OBJECTIVE: Recent brain-computer interface (BCI) assisted stroke rehabilitation protocols tend to focus on sensorimotor activity of the brain. Relying on evidence claiming that a variety of brain rhythms beyond sensorimotor areas are related to the extent of motor deficits, we propose to identify neural correlates of motor learning beyond sensorimotor areas spatially and spectrally for further use in novel BCI-assisted neurorehabilitation settings. APPROACH: Electroencephalographic (EEG) data were recorded from healthy subjects participating in a physical force-field adaptation task involving reaching movements through a robotic handle. EEG activity recorded during rest prior to the experiment and during pre-trial movement preparation was used as features to predict motor adaptation learning performance across subjects. MAIN RESULTS: Subjects learned to perform straight movements under the force-field at different adaptation rates. Both resting-state and pre-trial EEG features were predictive of individual adaptation rates with relevance of a broad network of beta activity. Beyond sensorimotor regions, a parieto-occipital cortical component observed across subjects was involved strongly in predictions and a fronto-parietal cortical component showed significant decrease in pre-trial beta-powers for users with higher adaptation rates and increase in pre-trial beta-powers for users with lower adaptation rates. SIGNIFICANCE: Including sensorimotor areas, a large-scale network of beta activity is presented as predictive of motor learning. Strength of resting-state parieto-occipital beta activity or pre-trial fronto-parietal beta activity can be considered in BCI-assisted stroke rehabilitation protocols with neurofeedback training or volitional control of neural activity for brain-robot interfaces to induce plasticity.


Assuntos
Adaptação Fisiológica/fisiologia , Interfaces Cérebro-Computador , Eletroencefalografia/métodos , Aprendizagem/fisiologia , Movimento/fisiologia , Desempenho Psicomotor/fisiologia , Estimulação Acústica/métodos , Adulto , Feminino , Humanos , Masculino , Adulto Jovem
9.
Artigo em Inglês | MEDLINE | ID: mdl-31110907

RESUMO

Current approaches on optimal spatio-spectral feature extraction for single-trial BCIs exploit mutual information based feature ranking and selection algorithms. In order to overcome potential confounders underlying feature selection by information theoretic criteria, we propose a non-parametric feature projection framework for dimensionality reduction that utilizes mutual information based stochastic gradient descent. We demonstrate the feasibility of the protocol based on analyses of EEG data collected during execution of open and close palm hand gestures. We further discuss the approach in terms of potential insights in the context of neurophysiologically driven prosthetic hand control.

10.
Neuroimage ; 110: 48-59, 2015 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-25623501

RESUMO

Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements are warranted and which ones are not supported by empirical evidence. We argue that the distinction between encoding and decoding models is not sufficient for this purpose: relevant features in encoding and decoding models carry a different meaning in stimulus- and in response-based experimental paradigms.We show that only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models trained on the same data, however, we obtain insights into causal relations beyond those that are implied by each individual model type. We illustrate the empirical relevance of our theoretical findings on EEG data recorded during a visuo-motor learning task.


Assuntos
Processamento de Imagem Assistida por Computador , Modelos Neurológicos , Neuroimagem/métodos , Neuroimagem/estatística & dados numéricos , Adulto , Algoritmos , Mapeamento Encefálico/métodos , Causalidade , Eletroencefalografia , Retroalimentação Sensorial , Humanos , Aprendizagem/fisiologia , Masculino , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão , Desempenho Psicomotor/fisiologia , Adulto Jovem
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