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+---
+layout: distill
+title: Denoising EMG signals
+description: The future of brain-computer interfaces rests on our ability to decode neural signals. Here we attempt to ensemble ML techniques to extract useful information from sEMG signals to improve downstream task performance.
+date: 2023-11-08
+htmlwidgets: true
+
+# Anonymize when submitting
+authors:
+ - name: Anonymous
+
+# authors:
+# - name: Prince Patel
+# url: "https://ppatel22.github.io/"
+# affiliations:
+# name: MIT
+
+
+# must be the exact same name as your blogpost
+bibliography: 2023-11-08-denoising-EMG-signals.bib
+
+# Add a table of contents to your post.
+# - make sure that TOC names match the actual section names
+# for hyperlinks within the post to work correctly.
+toc:
+ - name: Introduction
+ - name: Literature Review
+ - name: Methodology
+ - name: Results Analysis
+ subsections:
+ - name: Loss Function
+ - name: Increased Depth
+ - name: Latent Bottleneck
+ - name: Conclusion
+ # - name: Equations
+ # - name: Images and Figures
+ # subsections:
+ # - name: Interactive Figures
+ # - name: Citations
+ # - name: Footnotes
+ # - name: Code Blocks
+ # - name: Layouts
+ # - name: Other Typography?
+
+# Below is an example of injecting additional post-specific styles.
+# This is used in the 'Layouts' section of this post.
+# If you use this post as a template, delete this _styles block.
+_styles: >
+ .fake-img {
+ background: #bbb;
+ border: 1px solid rgba(0, 0, 0, 0.1);
+ box-shadow: 0 0px 4px rgba(0, 0, 0, 0.1);
+ margin-bottom: 12px;
+ }
+ .fake-img p {
+ font-family: monospace;
+ color: white;
+ text-align: left;
+ margin: 12px 0;
+ text-align: center;
+ font-size: 16px;
+ }
+---
+## Introduction
+Brain-machine interfaces (BCIs) have the potential to revolutionize human-computer interaction by decoding neural signals for real-time control of external devices. However, the current state of BCI technology is constrained by numerous challenges that have limited widespread adoption beyond clinical settings thus far. A critical barrier remains the intrinsically high signal-to-noise ratio (SNR) present in nerve recordings, which introduces substantial noise corruption that masks the relevant neural signals needed for effective device control. To address this persistent issue and work toward unlocking the full capabilities of BCIs for practical real-world application, significant advancements have been actively pursued in both hardware and software components underlying state-of-the-art systems.
+
+Innovations on the hardware front aim to obtain higher-fidelity measurements of neural activity, providing cleaner inputs for software-based decoding algorithms. Novel sensor materials, unconventional device architectures, and integrated on-chip processing have shown promise toward this goal. For example, advancing the conformality and resolution of electrode arrays through new nanomaterials facilitates more targeted recordings with enhanced SNR by achieving closer neuron proximity and tissue integration. Novel satellite electrode configurations have also demonstrated derivations less susceptible to artifacts. While these approaches indicate positive directionality, substantial room remains for developing next-generation hardware able to circumvent the most fundamental limitations imposed by volume conduction effects that dominate nerve signal propagation physics.
+
+Complementing these efforts, software techniques present immense potential to extract meaningful patterns straight from noisy raw recordings. The crux lies in designing sophisticated algorithms that can effectively denoise and transform complex neural data into compact, salient representations to feed downstream interpretable models. Myriad computational methods have been investigated spanning modeling, preprocessing, feature encoding, decoding, and prediction stages across model architectures. For preprocessing, techniques like low/high-pass filters, Fourier transforms, wavelet decompositions, empirical mode decompositions, and various outlier removal methods help restrict signal components to relevant frequency bands and statistics. Building upon these preprocessed signals, advanced unsupervised and self-supervised representation learning algorithms can then produce high-level abstractions as robust inputs for downstream tasks such as gesture classification. The proposed study aims to contribute uniquely to this mission by developing a novel framework tailored for preprocessing surface EMG signals. By focusing on noninvasive surface recordings, findings could generalize toward unlocking adoptable BCIs beyond strict clinical environments. Additionally, optimizing a sophisticated denoising autoencoder architecture reinforced with latest self-attention mechanisms will enrich encoded representations. Overall, this research anticipates advancing signal processing fundamentals underlying next-generation BCI systems positioned to transform human-computer interaction through widely accessible, adaptable platforms.
+
+## Literature Review
+The recent debut of BrainBERT in early 2023 signifies a potentially transformative advancement in decoding complex physiological signals by employing an innovative transformer architecture tailored for multivariate neural time series analysis. Initial evaluations demonstrate unmatched state-of-the-art performance in extracting robust, salient features from raw intracranial primate recordings for downstream gesture classification, substantially outperforming prior established methods on proprietary datasets. However, several key limitations currently temper sweeping conclusions regarding real-world viability and require expanded validation. Perhaps most critically, the intracranial modalities utilized pose far too invasive for serious consideration within mainstream wearable interfaces. Additionally, the restricted dataset comprises only five subjects performing six rudimentary gesture classes under tightly controlled conditions unlikely replicable outside laboratories. Finally, while posting strong isolated accuracies on a singular decoding task, analysis remains limited assessing encoding versatility across diverse downstream objectives to qualify expected generalizability.
+
+These nascent outcomes build upon seminal prior art pioneering self-attentive neural network frameworks for combating pervasive noise corruptions. Specifically, the 2021 study “DAEMA: Denoising Autoencoder with Mask Attention” introduced an innovative architecture leveraging trainable gating of input feature relevance based on missingness patterns. Results demonstrated state-of-the-art reconstruction capabilities from artificially corrupted EEG benchmarks by focusing model representations only on reliable data subsets. This novel direction set the foundation for subsequent efforts translating concepts toward filtering realistic physiological noise toward wearable applications.
+
+Present work seeks to advance signal encoding breakthroughs within this mission by implementing DAEMA-inspired attention components for multi-channel surface EMG data. The overarching motivation is developing an optimized model capable of extracting robust representations from notoriously noisy recordings that sufficiently retain nuanced muscle activation details critical for control inference. By concentrating learned encodings exclusively on clean input sections, downstream analyses from subtle gesture delineation to complex modeling of coordination deficiencies may become tractable where previously obstructed by artifacts. Outcomes would carry profound implications for unlocking ubiquitous responsive assistive technologies.
+
+## Methodology
+From a large sEMG dataset , I extracted the pattern recognition (PR) recordings for model development and testing. Specifically, I utilized one second long HD-sEMG recordings sampled at 2048 Hz. These data encompassed 256 electrode channel readings captured while subjects performed 34 distinct hand gestures. In total, twenty subjects completed two sessions each, yielding over one hundred gesture recording samples per person. Due to computational constraints, my preliminary analysis focused solely on the data from the first two subjects for training and validation.
+
+As a preprocessing step before feeding signals into my model, I first converted the 1D raw temporal traces into 2D spectrogram representations via short-time Fourier transforms. This translation from time domain to frequency domain was motivated by observations in the BrainBERT study, where the authors empirically found that presenting spectral depictions enabled superior feature extraction. To further improve conditioning, I normalized the matrix values using the global dataset mean and standard deviation.
+
+My methodology centered on designing and training an autoencoder architecture for denoising tasks. This system was composed of an encoder model to map inputs into a lower-dimensional latent space and a partner decoder model to subsequently reconstruct the original input. Specifically, the encoder segment utilizes a multi-headed self-attention transformer layer to reweight the relative importance of input spectrogram features reflective of signal clarity versus noise. By computing dot products between each time-frequency bin, the attention heads assign higher relevance weighting to bins more strongly correlated with other clear regions of the input. In this way, the model focuses on interconnections indicative of muscle physiology rather than random artifacts. Encoder feed-forward layers subsequently compress this reweighted representation into a low-dimensional latent embedding capturing core aspects. Batch normalization and non-linearities aid training convergence. Critically, this forces the model to encode only the most essential patterns, with excess noise ideally filtered out.
+
+The output then passes through a series of linear layers with ReLU activations to compress the data into a 28-dimensional latent representation (versus 56 originally). Paired with the encoder is an LSTM-based decoder that sequentially generates the full reconstructed spectrogram by capturing temporal dynamics in the latent encoding. Specifically, two bidirectional LSTM blocks first extract forward and backward sequential dependencies. A final linear layer then projects this decoding to match the original spectrogram shape. Since spectrograms consist strictly of positive values, the decoder output is passed through a final ReLU layer as well to enforce non-negativity.
+
+For training, I used a custom loss function defined as a variant of mean absolute error (MAE), with additional multiplicative penalties for overestimations to strongly discourage noise injection. While mean squared error (MSE) is conventionally used for regressors, I found that it failed to properly penalize deviations in the spectrogram context as values were restricted between zero and one. Moreover, initial models tended to overshoot guesses, spectrally spreading energy across additional erroneous frequency bands - a highly undesirable artifact for signal clarity. The custom loss thus applies much harsher penalties for such over-predictions proportional to their deviation magnitude. The purpose of the loss function is to compare the model’s output to the preprocessed signal, not the original raw input. This is what makes a denoising autoencoder different from a traditional autoencoder. So, this autoencoder is being tested on its ability to reconstruct the signal from a compressed latent representation and to ignore the noise removed by manual preprocessing of the signal.
+
+## Results Analysis
+In terms of model optimization, I incrementally adapted components like attention heads, linear layers in the encoder, LSTM layers in the decoder, and loss function parameters to improve performance. Below are some results collected during experiments, with relevant hyperparameter values listed. For context, the first signal from the validation is visualized below. Note that all experiments were done on the same sample.
+
+
+
+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image13.png" class="img-fluid" %}
+
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image7.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image10.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image9.png" class="img-fluid" %}
+
+
+ Diagrams are in the following order: raw signal waveform, preprocessed signal waveform, raw spectrogram, preprocessed spectrogram.
+
+
+
+### Loss Function
+
+MSE loss function:
+
+Applying MSE during training resulted in substantial overpredictions within reconstructed spectrograms. As shown, the final time-domain trace from the poorly constrained model contains heavy ambient noise contamination spanning multiple frequency bands – an undesirable artifact significantly corrupting signal clarity and interpretation. This confirms the need to explicitly restrict amplification predictions to retain fidelity.
+
+
+
+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image18.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image24.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image17.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image14.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image6.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image2.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image20.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image8.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image11.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image16.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image19.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image15.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image5.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image25.png" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image27.jpg" class="img-fluid" %}
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+ {% include figure.html path="assets/img/2023-11-08-denoising-EMG-signals/image26.jpg" class="img-fluid" %}
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