text: The electroencephalography classifier is the most important component of brain-computer interface based systems. There are two major problems hindering the improvement of it. First, traditional methods do not fully exploit multimodal information. Second, large-scale annotated EEG datasets are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Herein, we propose a novel deep transfer learning approach to solve these two problems. First, we model cognitive events based on EEG data by characterizing the data using EEG optical flow, which is designed to preserve multimodal EEG information in a uniform representation. Second, we design a deep transfer learning framework which is suitable for transferring knowledge by joint training, which contains a adversarial network and a special loss function. The experiments demonstrate that our approach, when applied to EEG classification tasks, has many advantages, such as robustness and accuracy.
  @cite_13: Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.
  @cite_9: 
  @cite_1: Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.
  @cite_0: 
  @cite_19: Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective brain-computer interfaces in real-world applications. On the other hand, it is very time consuming and expensive to acquire a large number of subject-specific labeled data for learning subject-specific models. In this paper, we propose to build personalized EEG-based affective models without labeled target data using transfer learning techniques. We mainly explore two types of subject-to-subject transfer approaches. One is to exploit shared structure underlying source domain (source subject) and target domain (target subject). The other is to train multiple individual classifiers on source subjects and transfer knowledge about classifier parameters to target subjects, and its aim is to learn a regression function that maps the relationship between feature distribution and classifier parameters. We compare the performance of five different approaches on an EEG dataset for constructing an affective model with three affective states: positive, neutral, and negative. The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31 in comparison with a conventional generic classifier with 56.73 in average.
  @cite_15: Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings.
  @cite_10: Transfer learning (TL) has gained significant interests recently in brain computer interface (BCI) as a key approach to design robust predictors for cross-subject and cross-experiment prediction of the brain activities in response to cognitive events. We carried out in this.aper the first comprehensive investigation of the transferability of deep convolutional neural network (CNN) for cross-subject and cross-experiment prediction of image Rapid Serial Visual Presentation (RSVP) events. We show that for both cross-subject and cross-experiment predictions, all convolutional layers and fully connected layers contain both general and subject experiment-specific features and transfer learning with weights fine-tuning can improve the prediction performance over that without transfer. However, for cross-subject prediction, the convolutional layers capture more subject-specific features, whereas for cross-experiment prediction, the convolutional layers capture more general features across experiment. Our study provides important information that will guide the design of more sophisticated deep transfer learning algorithms for EEG based classifications in BCI applications.
  @cite_12: 
summary: Transfer learning enables the use of different domains, tasks, and distributions for training and testing @cite_0 . @cite_12 reviewed the current state-of-the-art transfer learning approaches in BCI. @cite_10 transferred general features via a convolutional network across subjects and experiments. @cite_19 applied kernel principle analysis and transductive parameter transfer to identify the relationships between classifier parameter vectors across subjects. @cite_9 evaluated the transferability between subjects by calculating distance and transferred knowledge in comparable feature spaces to improve accuracy. @cite_15 proposed an approach which can simultaneously transfer knowledge across domains and tasks. @cite_1 and @cite_13 attempted the learning of transferable features by embedding task-specific layers in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched.