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Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many applications such as image analysis, speech recognition and text understanding. It uses supervised and unsupervised strategies to learn multi-level representations and features in hierarchical architectures for the tasks of classification and pattern recognition. Recent development in sensor networks and communication technologies has enabled the collection of big data. Although big data provides great opportunities for a broad of areas including e-commerce, industrial control and smart medical, it poses many challenging issues on data mining and information processing due to its characteristics of large volume, large variety, large velocity and large veracity. In the past few years, deep learning has played an important role in big data analytic solutions. In this paper, we review the emerging researches of deep learning models for big data feature learning. Furthermore, we point out the remaining challenges of big data deep learning and discuss the future topics

5类癫痫脑电数据，脑电数据是印度学家Varun Bajaj和Ram Bilas Pachori对正常人和癫痫病患者测试的数据。脑电信号数据由五个子集组成，分别为Z，O，N，F，S，每个脑电子集包含100个信道序列，每个信道持续时间为23.6秒,信号采样点是4097个数据点。