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Mar 12, Aug 8, Initial commit.We describe first a simple deep convolutional neural network DCNN with a five-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG spectrograms from idiopathic patients and healthy controls.
We treat the data as in audio or image classification problems where deep networks have proven successful by exploiting invariances and compositional features in the data. PD-conversion classification problem. The trained classifier can also be used to generate synthetic spectrograms using the DeepDream algorithm to study what time-frequency features are relevant for classification.
We find these to be bursts in the theta band together with a decrease of bursting in the alpha band in future RBD converters i. From this first study, we conclude that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, offering physiological insights and enabling the identification of clinically relevant biomarkers.
RBD is a parasomnia characterized by intense dreams with during REM sleep without muscle atonia 1i.
Idiopathic RBD occurs in the absence of any neurological disease or other identified cause, is male-predominant and its clinical course is generally chronic progressive 2. Several longitudinal studies conducted in sleep centers have shown that most patients diagnosed with the idiopathic form of RBD will eventually be diagnosed with a neurological disorder such as Parkinson disease PD or dementia with Lewy bodies DLB 1 — 4.
PD with RBD is characterized by more profound and extensive pathology—not limited to the brainstem—, with higher synuclein deposition in both cortical and sub-cortical regions. Electroencephalographic EEG and magnetoencephalographic MEG signals contain rich information associated with functional processes in the brain.
To a large extent, progress in their analysis has been driven by the study of spectral features in electrode space, which has indeed proven useful to study the human brain in both health and disease. It is worth mentioning that the selection of disease characterizing features from spectral analysis is mostly done after an extensive search in the frequency-channel domain.
However, neuronal activity exhibits non-linear dynamics and non-stationarity across temporal scales that cannot be studied properly using classical approaches.
Tools capable of capturing the rich spatiotemporal hierarchical structures hidden in these signals are needed. In Ruffini et al. However, ideally we would like to use methods where the relevant features are found directly by the algorithms.
Deep learning algorithms are designed for the task of exploiting compositional structure in data 9. In past work, for example, deep feed-forward autoencoders have been used for the analysis of EEG data to address the issue of feature selection, with promising results Interestingly, deep learning techniques, in particular, and artificial neural networks in general are themselves bio-inspired by the brain—the same biological system generating the electric signals we aim to decode.
This suggests they may be well suited for the task. Deep recurrent neural networks RNNsare known to be potentially Turing complete [see, e.
In earlier work, a particular class of RNNs called Echo State Networks ESNs that combine the power of RNNs for classification of temporal patterns and ease of training 14 was used with good results with the problem at hand. The dynamics of the reservoir provides a feature representation map of the input signals into a much larger dimensional space in a sense much like a kernel method. PD patients was obtained using a relatively small dataset in Ruffini et al. The main limitations of this approach, in our view, are the computational cost of developing the reservoir dynamics of large random networks and the associated need for feature selection e.
In this paper we use a similar but simpler strategy as the one presented in Vilamala et al. In comparison to Vilamala et al. Indeed, we believe such a pre-training would initialize the filtering weights to detect object-like features not present in spectrograms.
The proposed method outperforms several shallow methods used for comparison as presented in the results section. Lastly, we employ deep-learning visualization techniques for the interpretation of results. Once a network has been trained, one would like to understand what are the key features it is picking up from the data for classification. We show below how this can be done in the context of EEG spectrogram classification, and how it can be helpful in identifying physiologically meaningful features that would be hard to select by hand.
This is also very important for the clinical translation of such techniques, since black-box approaches have been extensively criticized. Our goal here will be to train a network to classify subjects from the EEG spectrograms recorded at baseline in binary problems, with classification labels such as HC healthy controlPD idiopathic RBD who will later convert to PDetc. Here we explore first a deep learning approach inspired by recent successes in image classification using deep convolutional neural networks designed to exploit invariances and capture compositional features in the data [see e.
These systems have been largely developed to deal with image data, i. Thus, inputs to such networks are data cubes multichannel stacked images.Convolutional neural network, also known as convnets or CNN, is a well-known method in computer vision applications.
This type of architecture is dominant to recognize objects from a picture or video. In this tutorial, you will learn how to construct a convnet and how to use TensorFlow to solve the handwritten dataset. Nowadays, Facebook uses convnet to tag your friend in the picture automatically. A convolutional neural network is not very difficult to understand. An input image is processed during the convolution phase and later attributed a label.
A typical convnet architecture can be summarized in the picture below. First of all, an image is pushed to the network; this is called the input image. Then, the input image goes through an infinite number of steps; this is the convolutional part of the network.
Finally, the neural network can predict the digit on the image. An image is composed of an array of pixels with height and width. A grayscale image has only one channel while the color image has three channels each one for Red, Green, and Blue. A channel is stacked over each other. In this tutorial, you will use a grayscale image with only one channel. Each pixel has a value from 0 to to reflect the intensity of the color. For instance, a pixel equals to 0 will show a white color while pixel with a value close to will be darker.
The picture below shows how to represent the picture of the left in a matrix format. Note that, the original matrix has been standardized to be between 0 and 1. For darker color, the value in the matrix is about 0. Convolutional operation The most critical component in the model is the convolutional layer. This part aims at reducing the size of the image for faster computations of the weights and improve its generalization.
During the convolutional part, the network keeps the essential features of the image and excludes irrelevant noise.
For instance, the model is learning how to recognize an elephant from a picture with a mountain in the background.Time-series data arise in many fields including finance, signal processing, speech recognition and medicine.
A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. For example, if one is dealing with signals i. A similar situation arises in image classification, where manually engineered features obtained by applying a number of filters could be used in classification algorithms.
During training, the CNN learns lots of "filters" with increasing complexity as the layers get deeper, and uses them in a final classifier. In this blog post, I will discuss the use of deep leaning methods to classify time-series data, without the need to manually engineer features. The dataset contains the raw time-series data, as well as a pre-processed one with engineered features. I will compare the performance of typical machine learning algorithms which use engineered features with two deep learning methods convolutional and recurrent neural networks and show that deep learning can surpass the performance of the former.
I have used Tensorflow for the implementation and training of the models discussed in this post. In the discussion below, code snippets are provided to explain the implementation.
For the complete code, please see my Github repository. There are 9 channels in this case, which include 3 different acceleration measurements for each 3 coordinate axes. First, we construct placeholders for the inputs to our computational graph:. The convolutional layers are constructed using one-dimensional kernels that move through the sequence unlike images where 2d convolutions are used.
These kernels act as filters which are being learned during training. As in many CNN architectures, the deeper the layers get, the higher the number of filters become. Each convolution is followed by pooling layers to reduce the sequence length. Below is a simple picture of a possible CNN architecture that can be used:. Once the last layer is reached, we need to flatten the tensor and feed it to a classifier with the right number of neurons in the above picture. Then, the classifier outputs logitswhich are used in two instances:.
The rest of the implementation is pretty typical, and involve feeding the graph with batches of training data and evaluating the performance on a validation set. Finally, the trained model is evaluated on the test set.
Since this problem also involves a sequence of similar sorts, an LSTM is a great candidate to be tried. This size parameter is chosen to be larger than the number of channels. This is in a way similar to embedding layers in text applications where words are embedded as vectors from a given vocabulary. For the implementation, the placeholders are the same as above. The below code snippet implements the LSTM layers:. There is an important technical detail in the above snippet.
The rest is pretty standard for LSTM implementations, involving construction of layers including dropout for regularization and then an initial state. The next step is to implement the forward pass through the network and the cost function.This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K - Nearest Neighbor.
A set of tools to analyze and create charts from Muse EEG devices. Detect EEG artifacts, outliers, or anomalies using supervised machine learning. Mobile Robot controlled using Neurosky Mindwave headset. This repository contains the data collection and robot control scripts developed using C.
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Dream prediction by using neural networks on EEG time series collected from sleeping humans. Add a description, image, and links to the eeg-classification topic page so that developers can more easily learn about it.
Curate this topic. To associate your repository with the eeg-classification topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 50 public repositories matching this topic Language: All Filter by language. Sort options. Star Code Issues Pull requests. Updated May 13, Python. Updated Nov 30, Python. Updated Jul 10, Jupyter Notebook. Updated Dec 12, Jupyter Notebook.
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Updated Mar 1, Python. A general matlab framework for EEG data classification. Updated Jan 9, Python. Updated Apr 5, Python. It's a tensorflow implemention for EEGNet.
Updated Jun 29, Python. Star 9. Updated Aug 21, Jupyter Notebook. Towards Turnkey Brain-Computer Interfaces. Star 7. Updated Feb 27, Python. Eye open and close classification using Machine Learning.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
If nothing happens, download the GitHub extension for Visual Studio and try again. The aim of this project is to. Then, one can simply import any model and configure it as. Compile the model with the associated loss function and optimizer in our case, the categorical cross-entropy and Adam optimizer, respectively.
Then fit the model and predict on new test data. To reproduce the EEGNet single-trial feature relevance results as we reported in download and install DeepExplain located [here]which implements a variety of relevance attribution methods both gradient-based and perturbation-based.
A sketch of how to use it is given below:. If you use the EEGNet model in your research and found it helpful, please cite the following paper:. Similarly, if you use the ShallowConvNet or DeepConvNet models and found them helpful, please cite the following paper:. This project is governed by the terms of the Creative Commons Zero 1. You should have received a copy of the Agreement with a copy of this software.
Your use or distribution of ARL EEGModels, in both source and binary form, in whole or in part, implies your agreement to abide by the terms set forth in the Agreement in full. Other portions of this project are subject to domestic copyright protection under 17 USC Sec. Those portions are licensed under the Apache 2.
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Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit May 13, The aim of this project is to provide a set of well-validated CNN models for EEG signal processing and classification facilitate reproducible research and enable other researchers to use and compare these models as easy as possible on their data Requirements Python either 2.
Both the original model and the revised model are implemented.This post was moved to a different board that fits your topic of discussion a bit better. No action is needed on your part; you can continue the conversation as normal here. You can continue the conversation right in this thread. Be sure to click Accept as Solution to mark helpful posts to help other users locate important info. Also, don't forget to give Kudos for great content!
As for why people might not be responding to your post, your question is very specific and not directly related to GitHub where this forum specialized in GitHub-related topics. You might have better luck in communities that specialize in Tensorflow or Neural Networks or if you expand the information around what you're trying to do and where you find yourself stuck.
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Time series classification with Tensorflow
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