# Time-Frequency Feature Embedding with Deep Metric Learning

This example shows how to use deep metric learning with a supervised contrastive loss to construct feature embeddings based on a time-frequency analysis of electroencephalographic (EEG) signals. The learned time-frequency embeddings reduce the dimensionality of the time-series data by a factor of 16. You can use these embeddings to classify EEG time-series from persons with and without epilepsy using a support vector machine classifier.

### Deep Metric Learning

Deep metric learning attempts to learn a nonlinear feature embedding, or encoder, that reduces the distance (a metric) between examples from the same class and increases the distance between examples from different classes. Loss functions that work in this way are often referred to as contrastive. This example uses supervised deep metric learning with a particular contrastive loss function called the normalized temperature-scaled cross-entropy loss [3],[4],[8]. The figure shows the general workflow for this supervised deep metric learning.

*Positive pairs* refer to training samples with the same label, while *negative pairs* refer to training samples with different labels. A distance, or similarity, matrix is formed from the positive and negative pairs. In this example, the cosine similarity matrix is used. From these distances, losses are computed and aggregated (reduced) to form a single scalar-valued loss for use in gradient-descent learning.

Deep metric learning is also applicable in weakly supervised, self-supervised, and unsupervised contexts. There is a wide variety of distance (metrics) measures, losses, reducers, and regularizers that are employed in deep metric learning.

### Data — Description, Attribution, and Download Instructions

The data used in this example is the Bonn EEG Data Set. The data is currently available at EEG Data Download and Ralph Andrzejak's EEG data download page. See Ralph Andrzejak's EEG data for legal conditions on the use of the data. The authors have kindly permitted the use of the data in this example.

The data in this example were first analyzed and reported in:

The data consists of five sets of 100 single-channel EEG recordings. The resulting single-channel EEG recordings were selected from 128-channel EEG recordings after visually inspecting each channel for obvious artifacts and satisfying a weak stationarity criterion. See the linked paper for details.

The original paper designates these five sets as A-E. Each recording is 23.6 seconds in duration sampled at 173.61 Hz. Each time series contains 4097 samples. The conditions are as follows:

A -- Normal subjects with eyes open

B -- Normal subjects with eyes closed

C -- Seizure-free recordings from patients with epilepsy. Recording from hippocampus in the hemisphere opposite the epileptogenic zone

D -- Seizure-free recordings obtained from patients with epilepsy. Recordings from epileptogenic zone.

E - Recordings from patients with epilepsy showing seizure activity.

The zip files corresponding to this data are labeled as z.zip (A), o.zip (B), n.zip (C), f.zip (D), and s.zip (E).

The example assumes you have downloaded and unzipped the zip files into folders named `Z`

, `O`

, `N`

, `F`

, and `S`

respectively. In MATLAB® you can do this by creating a parent folder and using that as the `OUTPUTDIR`

variable in the `unzip`

command. This example uses the folder designated by MATLAB as `tempdir`

as the parent folder. If you choose to use a different folder, adjust the value of `parentDir`

accordingly. The following code assumes that all the .zip files have been downloaded into `parentDir`

. Unzip the files by folder into a subfolder called `BonnEEG`

.

parentDir = tempdir; cd(parentDir) mkdir('BonnEEG') dataDir = fullfile(parentDir,'BonnEEG'); unzip('z.zip',dataDir) unzip('o.zip',dataDir) unzip('n.zip',dataDir) unzip('f.zip',dataDir) unzip('s.zip',dataDir)

### Creating in-memory data and labels

The individual EEG time series are stored as .txt files in each of the `Z`

, `N`

, `O`

, `F`

, and `S`

folders under `dataDir`

. Use a `tabularTextDatastore`

to read the data. Create a tabular text datastore and create a categorical array of signal labels based on the folder names.

tds = tabularTextDatastore(dataDir,'IncludeSubfolders',true,'FileExtensions','.txt');

The zip files were created on a macOS and accordingly there may be a MACOSX folder created with unzip that results in extra files. If those exist, remove them.

```
extraTXT = contains(tds.Files,'__MACOSX');
tds.Files(extraTXT) = [];
```

Create labels for the data based on the first letter of the text file name.

`labels = filenames2labels(tds.Files,'ExtractBetween',[1 1]);`

Each `read`

of the tabular text datastore creates a table containing the data. Create a cell array of all signals reshaped as row vectors so they conform with the deep learning networks used in the example.

ii = 1; eegData = cell(numel(labels),1); while hasdata(tds) tsTable = read(tds); ts = tsTable.Var1; eegData{ii} = reshape(ts,1,[]); ii = ii+1; end

### Time-Frequency Feature Embedding Deep Network

Here we construct a deep learning network that creates an embedding of the input signal based on a time-frequency analysis.

TFnet = [sequenceInputLayer(1,'MinLength',4097,'Name',"input") cwtLayer('SignalLength',4097,'IncludeLowpass',true,'Wavelet','amor',... 'FrequencyLimits',[0 0.23]) convolution2dLayer([5,10],1,'stride',2) maxPooling2dLayer([5,10]) convolution2dLayer([5,10],5,'Padding','same') maxPooling2dLayer([5,10]) batchNormalizationLayer reluLayer convolution2dLayer([5,10],10,'Padding','same') maxPooling2dLayer([2,4]) batchNormalizationLayer reluLayer flattenLayer globalAveragePooling1dLayer fullyConnectedLayer(256)]; TFnet = dlnetwork(TFnet);

After the input layer, the network obtains the continuous wavelet transform (CWT) of the data using the analytic Morlet wavelet. The output of `cwtLayer`

is the magnitude of the CWT, or scalogram. Unlike the analyses in [1],[2], and [7], no pre-processing bandpass filter is used in this network. Instead, the CWT is obtained only over the frequency range of [0.0, 0.23] cycles/sample which is equivalent to [0,39.93] Hz for the sample rate of 173.61 Hz. This is the approximate range of the bandpass filter applied to the data before analysis in [1]. After the network obtains the scalogram, the network cascades a series of 2-D convolutional, batch normalization, and RELU layers. The final layer is a fully connected layer with 256 output units. This results in a 16-fold reduction in the size of the input. See [7] for another scalogram-based analysis of this data and [2] for another wavelet-based analysis using the tunable Q-factor wavelet transform.

### Differentiating Normal, Pre-seizure, and Seizure EEG

Given the five conditions present in the data, there are multiple meaningful and clinically informative ways to partition the data. One relevant way is to group the Z and O labels (non-epileptic subjects with eyes open and closed) as "Normal". Similarly, the two conditions recorded in the persons with epilepsy without overt seizure activity (N and F) may be grouped as "Pre-seizure". Finally, we designate the recordings obtained in epileptic subjects with seizure activity as "Seizure". To create labels, which may be cast to numeric values during training, designate these three classes as:

0 -- "Normal"

1 -- "Pre-seizure"

2 -- "Seizure"

Partition the data into training and test sets. First, create the new labels in order to partition the data. Examine the number of examples in each class.

labelsPS = labels; labelsPS = removecats(labelsPS,{'F','N','O','S','Z'}); labelsPS(labels == categorical("Z") | labels == categorical("O")) = categorical("0"); labelsPS(labels == categorical("N") | labels == categorical("F")) = categorical("1"); labelsPS(labels == categorical("S")) = categorical("2"); labelsPS(isundefined(labelsPS)) = []; summary(labelsPS)

0 200 1 200 2 100

The resulting classes are unbalanced with twice as many signals in the "Normal" and "Pre-seizure" categories as in the "Seizure" category. Partition the data for training the encoder and the hold-out test set. Allocate 80% of the data to the training set and 20% to the test set.

idxPS = splitlabels(labelsPS,[0.8 0.2]); TrainDataPS = eegData(idxPS{1}); TrainLabelsPS = labelsPS(idxPS{1}); testDataPS = eegData(idxPS{2}); testLabelsPS = labelsPS(idxPS{2});

### Training the Encoder

To train the encoder, set `trainEmbedder`

to `true`

. To skip the training and load a pretrained encoder and corresponding embeddings, set `trainEmbedder`

to `false`

and go to the Test Data Embeddings section.

trainEmbedder = true;

Because this example uses a custom loss function, you must use a custom training loop. To manage data through the custom training loop, use a `signalDatastore`

(Signal Processing Toolbox) with a custom read function that normalizes the input signals to have zero mean and unit standard deviation.

if trainEmbedder sdsTrain = signalDatastore(TrainDataPS,MemberNames = string(TrainLabelsPS)); transTrainDS = transform(sdsTrain,@(x,info)helperReadData(x,info),'IncludeInfo',true); end

Train the network by measuring the normalized temperature-controlled cross-entropy loss between embeddings obtained from identical classes (corresponding to positive pairs) and disparate classes (corresponding to negative pairs) in each mini-batch. The custom loss function computes the cosine similarity between each training example, obtaining a M-by-M similarity matrix, where M is the mini-batch size. The function computes the normalized temperature-controlled cross entropy for the similarity matrix with the *temperature* parameter equal to 0.07. The function calculates the scalar loss as the mean of the mini-batch losses.

### Specify Training Options

The model parameters are updated based on the loss using an Adam optimizer.

Train the encoder for 150 epochs with a mini-batch size of 50, a learning rate of 0.001, and an L2-regularization rate of 0.01.

if trainEmbedder NumEpochs = 150; minibatchSize = 50; learnRate = 0.001; l2Regularization = 1e-2; end

Calculate the number of iterations per epoch and the total number of iterations to display training progress.

if trainEmbedder numObservations = numel(TrainDataPS); numIterationsPerEpoch = floor(numObservations./minibatchSize); numIterations = NumEpochs*numIterationsPerEpoch; end

Create a `minibatchqueue`

(Deep Learning Toolbox) object to manage data flow through the custom training loop.

if trainEmbedder numOutputs = 2; mbqTrain = minibatchqueue(transTrainDS,numOutputs,... 'minibatchSize',minibatchSize,... 'OutputAsDlarray',[1,1],... 'minibatchFcn',@processMB,... 'OutputCast',{'single','single'},... 'minibatchFormat', {'CBT','B'}); end

Train the encoder.

if trainEmbedder progress = "final-loss"; if progress == "training-progress" figure lineLossTrain = animatedline; ylim([0 inf]) xlabel("Iteration") ylabel("Loss") grid on end % Initialize some training loop variables trailingAvg = []; trailingAvgSq = []; iteration = 1; lossByIteration = zeros(numIterations,1); % Loop over epochs and time the epochs start = tic; for epoch = 1:NumEpochs % Shuffle the mini-batches each epoch reset(mbqTrain) shuffle(mbqTrain) % Loop over mini-batches while hasdata(mbqTrain) % Get the next mini-batch and one-hot coded targets [dlX,Y] = next(mbqTrain); % Evaluate the model gradients and contrastive loss [gradients, loss, state] = dlfeval(@modelGradcontrastiveLoss,TFnet,dlX,Y); if progress == "final-loss" lossByIteration(iteration) = loss; end % Update the gradients with the L2-regularization rate idx = TFnet.Learnables.Parameter == "Weights"; gradients(idx,:) = ... dlupdate(@(g,w) g + l2Regularization*w, gradients(idx,:), TFnet.Learnables(idx,:)); % Update the network state TFnet.State = state; % Update the network parameters using an Adam optimizer [TFnet,trailingAvg,trailingAvgSq] = adamupdate(... TFnet,gradients,trailingAvg,trailingAvgSq,iteration,learnRate); % Display the training progress D = duration(0,0,toc(start),'Format','hh:mm:ss'); if progress == "training-progress" addpoints(lineLossTrain,iteration,loss) title("Epoch: " + epoch + ", Elapsed: " + string(D)) end iteration = iteration + 1; end disp("Training loss after epoch " + epoch + ": " + loss); end if progress == "final-loss" plot(1:numIterations,lossByIteration) grid on title('Training Loss by Iteration') xlabel("Iteration") ylabel("Loss") end end

Training loss after epoch 1: 1.4759 Training loss after epoch 2: 1.5684 Training loss after epoch 3: 1.0331 Training loss after epoch 4: 1.1621 Training loss after epoch 5: 0.70297 Training loss after epoch 6: 0.29956 Training loss after epoch 7: 0.42671 Training loss after epoch 8: 0.23963 Training loss after epoch 9: 0.021723 Training loss after epoch 10: 0.50336 Training loss after epoch 11: 0.34225 Training loss after epoch 12: 0.63325 Training loss after epoch 13: 0.31603 Training loss after epoch 14: 0.25883 Training loss after epoch 15: 0.52879 Training loss after epoch 16: 0.27623 Training loss after epoch 17: 0.070335 Training loss after epoch 18: 0.073039 Training loss after epoch 19: 0.2657 Training loss after epoch 20: 0.10312 Training loss after epoch 21: 0.33435 Training loss after epoch 22: 0.24089 Training loss after epoch 23: 0.083583 Training loss after epoch 24: 0.33138 Training loss after epoch 25: 0.006466 Training loss after epoch 26: 0.44036 Training loss after epoch 27: 0.028106 Training loss after epoch 28: 0.14215 Training loss after epoch 29: 0.018414 Training loss after epoch 30: 0.018228 Training loss after epoch 31: 0.026751 Training loss after epoch 32: 0.026275 Training loss after epoch 33: 0.13545 Training loss after epoch 34: 0.029467 Training loss after epoch 35: 0.0088911 Training loss after epoch 36: 0.12077 Training loss after epoch 37: 0.1113 Training loss after epoch 38: 0.14529 Training loss after epoch 39: 0.10718 Training loss after epoch 40: 0.10141 Training loss after epoch 41: 0.018227 Training loss after epoch 42: 0.0086456 Training loss after epoch 43: 0.025808 Training loss after epoch 44: 0.00021023 Training loss after epoch 45: 0.0013423 Training loss after epoch 46: 0.0020328 Training loss after epoch 47: 0.012152 Training loss after epoch 48: 0.00025792 Training loss after epoch 49: 0.0010626 Training loss after epoch 50: 0.0015668 Training loss after epoch 51: 0.00048469 Training loss after epoch 52: 0.00073284 Training loss after epoch 53: 0.00043141 Training loss after epoch 54: 0.0009649 Training loss after epoch 55: 0.00014656 Training loss after epoch 56: 0.00024468 Training loss after epoch 57: 0.00092313 Training loss after epoch 58: 0.00022878 Training loss after epoch 59: 6.3505e-05 Training loss after epoch 60: 5.0711e-05 Training loss after epoch 61: 0.0006025 Training loss after epoch 62: 0.00010356 Training loss after epoch 63: 0.00018479 Training loss after epoch 64: 0.00042666 Training loss after epoch 65: 6.88e-05 Training loss after epoch 66: 0.00019625 Training loss after epoch 67: 0.00064875 Training loss after epoch 68: 0.00017705 Training loss after epoch 69: 0.00086301 Training loss after epoch 70: 0.00044735 Training loss after epoch 71: 0.00099668 Training loss after epoch 72: 3.7804e-05 Training loss after epoch 73: 9.1751e-05 Training loss after epoch 74: 2.6748e-05 Training loss after epoch 75: 0.0012345 Training loss after epoch 76: 0.00019493 Training loss after epoch 77: 0.00058993 Training loss after epoch 78: 0.0024207 Training loss after epoch 79: 7.1345e-05 Training loss after epoch 80: 0.00015598 Training loss after epoch 81: 9.3623e-05 Training loss after epoch 82: 8.9839e-05 Training loss after epoch 83: 0.0024844 Training loss after epoch 84: 0.0001383 Training loss after epoch 85: 0.00027976 Training loss after epoch 86: 0.17246 Training loss after epoch 87: 0.61378 Training loss after epoch 88: 0.41423 Training loss after epoch 89: 0.35526 Training loss after epoch 90: 0.081963 Training loss after epoch 91: 0.09392 Training loss after epoch 92: 0.026856 Training loss after epoch 93: 0.18554 Training loss after epoch 94: 0.04293 Training loss after epoch 95: 0.0002686 Training loss after epoch 96: 0.0071139 Training loss after epoch 97: 0.0028931 Training loss after epoch 98: 0.029305 Training loss after epoch 99: 0.0080128 Training loss after epoch 100: 0.0018248 Training loss after epoch 101: 0.00012145 Training loss after epoch 102: 7.6166e-05 Training loss after epoch 103: 0.0001156 Training loss after epoch 104: 8.262e-05 Training loss after epoch 105: 0.00023958 Training loss after epoch 106: 0.00016227 Training loss after epoch 107: 0.00025268 Training loss after epoch 108: 0.0022929 Training loss after epoch 109: 0.00029386 Training loss after epoch 110: 0.00029691 Training loss after epoch 111: 0.00033467 Training loss after epoch 112: 5.31e-05 Training loss after epoch 113: 0.00013522 Training loss after epoch 114: 1.4335e-05 Training loss after epoch 115: 0.0015768 Training loss after epoch 116: 2.4165e-05 Training loss after epoch 117: 0.00031281 Training loss after epoch 118: 3.4592e-05 Training loss after epoch 119: 7.1151e-05 Training loss after epoch 120: 0.00020099 Training loss after epoch 121: 1.7647e-05 Training loss after epoch 122: 0.00010945 Training loss after epoch 123: 0.0012003 Training loss after epoch 124: 4.5947e-05 Training loss after epoch 125: 0.00043231 Training loss after epoch 126: 7.3228e-05 Training loss after epoch 127: 2.3522e-05 Training loss after epoch 128: 0.00014366 Training loss after epoch 129: 0.00010692 Training loss after epoch 130: 0.00066842 Training loss after epoch 131: 9.2536e-06 Training loss after epoch 132: 0.0007364 Training loss after epoch 133: 3.0709e-05 Training loss after epoch 134: 5.4056e-05 Training loss after epoch 135: 3.3361e-05 Training loss after epoch 136: 8.1937e-05 Training loss after epoch 137: 0.00012198 Training loss after epoch 138: 3.9838e-05 Training loss after epoch 139: 0.00025224 Training loss after epoch 140: 4.9974e-05 Training loss after epoch 141: 8.302e-05 Training loss after epoch 142: 2.009e-05 Training loss after epoch 143: 7.2674e-05 Training loss after epoch 144: 4.8355e-05 Training loss after epoch 145: 0.0008231 Training loss after epoch 146: 0.00017177 Training loss after epoch 147: 3.4427e-05 Training loss after epoch 148: 0.0095201 Training loss after epoch 149: 0.026009 Training loss after epoch 150: 0.071619

### Test Data Embeddings

Obtain the embeddings for the test data. If you set `trainEmbedder`

to `false`

, you can load the trained encoder and embeddings obtained using the `helperEmbedTestFeatures`

function.

if trainEmbedder finalEmbeddingsTable = helperEmbedTestFeatures(TFnet,testDataPS,testLabelsPS); else load('TFnet.mat'); %#ok<*UNRCH> load('finalEmbeddingsTable.mat'); end

Use a support vector machine (SVM) classifier with a Gaussian kernel to classify the embeddings.

template = templateSVM(... 'KernelFunction', 'gaussian', ... 'PolynomialOrder', [], ... 'KernelScale', 4, ... 'BoxConstraint', 1, ... 'Standardize', true); classificationSVM = fitcecoc(... finalEmbeddingsTable, ... "EEGClass", ... 'Learners', template, ... 'Coding', 'onevsone');

Show the final test performance of the trained encoder. The recall and precision performance for all three classes is excellent. The learned feature embeddings provide nearly 100% recall and precision for the normal (0), pre-seizure (1), and seizure classes (2). Each embedding represents a reduction in the input size from 4097 samples to 256 samples.

predLabelsFinal = predict(classificationSVM,finalEmbeddingsTable); testAccuracyFinal = sum(predLabelsFinal == testLabelsPS)/numel(testLabelsPS)*100

testAccuracyFinal = 100

hf = figure; confusionchart(hf,testLabelsPS,predLabelsFinal,'RowSummary','row-normalized',... 'ColumnSummary','column-normalized'); set(gca,'Title','Confusion Chart -- Trained Embeddings')

For completeness, test the cross-validation accuracy of the feature embeddings. Use five-fold cross validation.

partitionedModel = crossval(classificationSVM, 'KFold', 5); [validationPredictions, validationScores] = kfoldPredict(partitionedModel); validationAccuracy = ... (1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError'))*100

`validationAccuracy = `*single*
99

The cross-validation accuracy is also excellent at near 100%. Note that we have used all the 256 embeddings in the SVM model, but the embeddings returned by the encoder are always amenable to further reduction by using feature selection techniques such as neighborhood component analysis, minimum redundancy maximum relevance (MRMR), or principal component analysis. See Introduction to Feature Selection (Statistics and Machine Learning Toolbox) for more details.

### Summary

In this example, a time-frequency convolutional network was used as the basis for learning feature embeddings using a deep metric model. Specifically, the normalized temperature-controlled cross-entropy loss with cosine similarities was used to obtain the embeddings. The embeddings were then used with a SVM with a Gaussian kernel to achieve near perfect test performance. There are a number of ways this deep metric network can be optimized which are not explored in this example. For example, the size of the embeddings can likely be reduced further without affecting performance while achieving further dimensionality reduction. Additionally, there are a large number of similarity (metrics) measures, loss functions, regularizers, and reducers which are not explored in this example. Finally, the resulting embeddings are compatible with any machine learning algorithm. An SVM was used in this example, but you can explore the feature embeddings in the **Classification Learner** app and may find that another classification algorithm is more robust for your application.

### References

[1] Andrzejak, Ralph G., Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E. Elger. "Indications of Nonlinear Deterministic and Finite-Dimensional Structures in Time Series of Brain Electrical Activity: Dependence on Recording Region and Brain State." Physical Review E 64, no. 6 (2001). https://doi.org/10.1103/physreve.64.061907.

[2] Bhattacharyya, Abhijit, Ram Pachori, Abhay Upadhyay, and U. Acharya. "Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals." Applied Sciences 7, no. 4 (2017): 385. https://doi.org/10.3390/app7040385.

[3] Chen, Ting, Simon Kornblith, Mohammed Norouzi, and Geoffrey Hinton. "A Simple Framework for Contrastive Learning of Visual Representations." (2020). https://arxiv.org/abs/2002.05709

[4] He, Kaiming, Fan, Haoqi, Wu, Yuxin, Xie, Saining, Girschick, Ross. "Momentum Contrast for Unsupervised Visual Representation Learning." (2020). https://arxiv.org/pdf/1911.05722.pdf

[6] Musgrave, Kevin. "PyTorch Metric Learning" https://kevinmusgrave.github.io/pytorch-metric-learning/

[7] Türk, Ömer, and Mehmet Siraç Özerdem. “Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals.” *Brain Sciences* 9, no. 5 (2019): 115. https://doi.org/10.3390/brainsci9050115.

[8] Van den Oord, Aaron, Li, Yazhe, and Vinyals, Oriol. "Representation Learning with Contrastive Predictive Coding." (2019). https://arxiv.org/abs/1807.03748

function [grads,loss,state] = modelGradcontrastiveLoss(net,X,T) % This function is only for use in the "Time-Frequency Feature Embedding % with Deep Metric Learning" example. It may change or be removed in a % future release. % Copyright 2022, The Mathworks, Inc. [y,state] = net.forward(X); loss = contrastiveLoss(y,T); grads = dlgradient(loss,net.Learnables); loss = double(gather(extractdata(loss))); end function [out,info] = helperReadData(x,info) % This function is only for use in the "Time-Frequency Feature Embedding % with Deep Metric Learning" example. It may change or be removed in a % future release. % Copyright 2022, The Mathworks, Inc. mu = mean(x,2); stdev = std(x,1,2); z = (x-mu)./stdev; out = {z,info.MemberName}; end function [dlX,dlY] = processMB(Xcell,Ycell) % This function is only for use in the "Time-Frequency Feature Embedding % with Deep Metric Learning" example. It may change or be removed in a % future release. % Copyright 2022, The Mathworks, Inc. Xcell = cellfun(@(x)reshape(x,1,1,[]),Xcell,'uni',false); Ycell = cellfun(@(x)str2double(x),Ycell,'uni',false); dlX = cat(2,Xcell{:}); dlY = cat(1,Ycell{:}); end function testFeatureTable = helperEmbedTestFeatures(net,testdata,testlabels) % This function is only for use in the "Time-Frequency Feature Embedding % with Deep Metric Learning" example. It may change or be removed in a % future release. % Copyright 2022, The Mathworks, Inc. testFeatures = zeros(length(testlabels),256,'single'); for ii = 1:length(testdata) yhat = predict(net,dlarray(reshape(testdata{ii},1,1,[]),'CBT')); yhat= extractdata(gather(yhat)); testFeatures(ii,:) = yhat; end testFeatureTable = array2table(testFeatures); testFeatureTable = addvars(testFeatureTable,testlabels,... 'NewVariableNames',"EEGClass"); end function loss = contrastiveLoss(features,targets) % This function is for is only for use in the "Time-Frequency Feature % Embedding with Deep Metric Learning" example. It may change or be removed % in a future release. % % Replicates code in PyTorch Metric Learning % https://github.com/KevinMusgrave/pytorch-metric-learning. % Python algorithms due to Kevin Musgrave % Copyright 2022, The Mathworks, Inc. loss = infoNCE(features,targets); end function loss = infoNCE(embed,labels) ref_embed = embed; [posR,posC,negR,negC] = convertToPairs(labels); dist = cosineSimilarity(embed,ref_embed); loss = pairBasedLoss(dist,posR,posC,negR,negC); end function [posR,posC,negR,negC] = convertToPairs(labels) Nr = length(labels); % The following provides a logical matrix which indicates where % the corresponding element (i,j) of the covariance matrix of % features comes from the same class or not. At each (i,j) % coming from the same class we have a 1, at each (i,j) from a % different class we have 0. Of course the diagonal is 1s. labels = stripdims(labels); matches = (labels == labels'); % Logically negate the matches matrix to obtain differences. differences = ~matches; % We negate the diagonal of the matches matrix to avoid biasing % the learning. Later when we identify the positive and % negative indices, these diagonal elements will not be picked % up. matches(1:Nr+1:end) = false; [posR,posC,negR,negC] = getAllPairIndices(matches,differences); end function dist = cosineSimilarity(emb,ref_embed) emb = stripdims(emb); ref_embed = stripdims(ref_embed); normEMB = emb./sqrt(sum(emb.*emb,1)); normREF = ref_embed./sqrt(sum(ref_embed.*ref_embed,1)); dist = normEMB'*normREF; end function loss = pairBasedLoss(dist,posR,posC,negR,negC) if any([isempty(posR),isempty(posC),isempty(negR),isempty(negC)]) loss = dlarray(zeros(1,1,'like',dist)); return; end Temperature = 0.07; dtype = underlyingType(dist); idxPos = sub2ind(size(dist),posR,posC); pos_pair = dist(idxPos); pos_pair = reshape(pos_pair,[],1); idxNeg = sub2ind(size(dist),negR,negC); neg_pair = dist(idxNeg); neg_pair = reshape(neg_pair,[],1); pos_pair = pos_pair./Temperature; neg_pair = neg_pair./Temperature; n_per_p = negR' == posR; neg_pairs = neg_pair'.*n_per_p; neg_pairs(n_per_p==0) = -realmax(dtype); maxNeg = max(neg_pairs,[],2); maxPos = max(pos_pair,[],2); maxVal = max(maxPos,maxNeg); numerator = exp(pos_pair-maxVal); denominator = sum(exp(neg_pairs-maxVal),2)+numerator; logexp = log((numerator./denominator)+realmin(dtype)); loss = mean(-logexp,'all'); end function [posR,posC,negR,negC] = getAllPairIndices(matches,differences) % Here we just get the row and column indices of the anchor % positive and anchor negative elements. [posR, posC] = find(extractdata(matches)); [negR,negC] = find(extractdata(differences)); end

## See Also

### Apps

- Classification Learner (Statistics and Machine Learning Toolbox)

### Functions

`dlcwt`

|`cwtfilters2array`

|`cwt`