Application of Deep Learning for EEG

Introduction To Deep Learning:

Deep learning is part of machine learning methods based on learning data representations, as opposed to task-specific algorithms. It uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. It learns in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners and also learns multiple levels of representations that correspond to different levels of abstraction (LeCun, Bengio, & Hinton, 2015).

Some of the deep learning architectures are as follows:

• 1.

  Deep neural networks.

• 2.

  Deep belief networks.

• 3.

  Recurrent neural networks.

These architectures have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs. Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences.

In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face (Goodfellow, Bengio, Courville, & Bengio, 2016). Importantly, a deep learning process can learn which features to optimally place in which level on its own. The “deep” in “deep learning” refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feed forward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited. Deep learning architectures are often constructed with a greedy layer-by-layer method and help to disentangle these abstractions and pick out which features improve performance. For supervised learning tasks, deep learning methods obviate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures that remove redundancy in representation.

Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are neural history compressors and deep belief networks (Deng & Yu, 2014) .

While deep learning was first theorized in the 1980`s, there are two main reasons it has only recently become useful:

• 1.

  Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video.

• 2.

  Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.

Some of the applications of Deep learning are as follows (Deng & Yu, 2014):