Emg Signal Processing Matlab

[Saraid Madnick]

SignalProcessing Toolbox provides functions and apps to manage, analyze, preprocess, and extract features from uniformly and nonuniformly sampled signals. The toolbox includes tools for filter design and analysis, resampling, smoothing, detrending, and power spectrum estimation. You can use the Signal Analyzer app for visualizing and processing signals simultaneously in time, frequency, and time-frequency domains. With the Filter Designer app you can design and analyze FIR and IIR digital filters. Both apps generate MATLAB scripts to reproduce or automate your work.

Using toolbox functions, you can prepare signal datasets for AI model training by engineering features that reduce dimensionality and improve the quality of signals. You can access and process collections of files and large datasets using signal datastores. With the Signal Labeler app, you can annotate signal attributes, regions, and points of interest to create labeled signal sets. The toolbox supports GPU acceleration in addition to C/C++ and CUDA code generation for desktop prototyping and embedded system deployment.

Perform preprocessing, feature engineering, signal labeling, and dataset generation for machine learning and deep learning workflows. Use the Signal Labeler app to create ground truth datasets and extract features to train AI models.

Measure and extract distinctive features in signals including peaks, power, bandwidth, distortion, and signal statistics. Compute metrics related to pulses and transitions. Extract features for an entire dataset using the Signal Labeler app.

Design, analyze, and implement digital and analog filters. Use the Filter Designer app or the Design Filter live editor task to design a variety of digital FIR and IIR filters, such as lowpass, highpass, and bandstop.

Accelerate the execution of your signal processing algorithms using a Graphics Processing Unit (GPU). Generate portable C/C++ source code, standalone executables, or standalone applications from your MATLAB code.

[Comparison with cars at the end] For learning from scratch, I would not suggest a programming language alone, but instead the couple "teaching materials" (book, lecture notes) + "exercises with a specific programming language". So if you find a book that you like on "Python for computer vision with exercises" or "Image processing theory and practice with Matlab, that could be interesting starting points.Also, your programming tastes and skills may evolve, and learning a first programming language helps you learning a second one in general. But laziness sometimes drives you to sticking to your first language, reusing old librairies.Last, the purpose is important. In my case, I mostly engineer algorithms as prototypes and proofs of concepts, that can stay as them, or are turned into "solid programs" by people that are better at, and like better, programming with the rules-of-art in lower levels languages, depending on the target.

To that respect, Matlab is great at designing and fine tuning algorithms, possesses a lot of documentation and help that you can follow step-by-step, and enjoys a long list of contributed toolboxes, esp. at MatlabCentral FileExchange. When the workflow is set, if you care of speed, efficiency, etc., it is time to pass the algorithmic prototyping over to real programmers (C++, or lower level, which I can't do).

Globally, as long as you grow solid image processing skills, I would think what mostly differ between Matlab and Python are the cost and the trendiness. On my side, I would switch to Python for machine learning and data science, but I will stick to Matlab for most of my signal processing and image analysis works for a while.

I have spent the first 20 years of my career working extensively in MATLAB for signal processing applications. Six years ago I gravitated over to Python out of curiosity and it has since completely replaced Matlab as my tool of choice meeting all my needs for signal processing.

It's user friendly, once you really understand itsfunctionality and features. Jumping in without actually learning it can be very frustrating if coming from Matlab; but in particular once you learn how well and universally it handles iteration, it is quite beautiful!

The above main points are my perceptions based on my own experience which is always evolving and open to change, but let me also share some supporting evidence that have influenced my thinking to date (beyond my personal experience with both tools). I am not overly biased toward any of the tools other than these reasons; each on their own are fantastic accomplishments by the people involved in putting them together and I would be happy to be stuck on an island with any of these tools (between Matlab, Octave and Python) if it was an island where I still needed to solve challenging signal processing related problems. (The island of misfit transforms perhaps).

For the plot above, "LA" denotes linear algebra test models where Matlab wins on speed while the other lines where Python wins are with statistical models. A later plot as you read down the post shows that under parallel operation distributed across multiple processor cores, Matlab still wins with the LA models but with marginal difference in that scenario.

Julia has been posting benchmarks comparing Octave, Matlab and Python that captured my interest. This chart was made by the Julia folks (surprise, they are at the top of the run-time speed scale), but of interest to me was something I personally wanted to see-- how does Python compare to Matlab, and Matlab to Octave. So I was pleased to see that in considering switching from Matlab to Python that the speeds are comparable. Matlab is faster in some categories and slower than others, but overall the processing speed is similar in contrast to Matlab vs Octave. Not that Python is a choice when speed is the ultimate concern but I did want to know if there would be a significant penalty. I see that over time the reported results from Julia are changing (here is an earlier one from 2017) and not sure if that has to do with different versions of the tools or changes in the testing approach.

I am motivated to do my own benchmarking specific to common applications in signal processing since I have access to all three tools, perhaps as a Github repository as Chris Luengo has suggested in the comments. I like that idea, and will post a link here if progress is made on that front, or if I find other existing benchmarks more specific to signal processing tools.

Python is supported by a very large community in many areas beyond where Matlab participates (web design for example). The reason that it is a general purpose software development tool and not reserved just for mathematical algorithm development is specifically an advantage to building a larger community, given that with the right libraries (numpy and scipy in particular) it still provides all the powerful mathematical algorithm development with vector processing similar to Matlab. It is widely used in many fields and applications including specifically scientific computing and engineering, but has a broad user base of different disciplines. Given it is an open source user supported tool this is a huge strength in quickly finding the help you need to questions, or the tool you need for any given application (in most cases you can find it rather than build it, or build on it). This also contributes to writing applications faster. Below are some interesting datapoints suggesting a scale on the size of the relative communities:

The plot below was generated at the time of writing this at this link. These are the trending results for questions based on different languages posted to Stack Overflow. Based on the importance to me of a large user community (and the advantages I have experienced from that), I won't be jumping over to Julia anytime soon according to these same results, but could be a promising language to consider in the future:

So the above points were "frosting on the cake" once I understood that I could easily create all the signal processing applications and analysis, with all the graphics and integrated development features I enjoyed with MATLAB, with a very intuitive easy to read and write language.

MATLAB is one of the most useful & successful software tools developed for scientists and engineers. From a DSP point of view, its syntax is simply the best. And, image processing is one of its strongest parts in addition to Simulink. However, it has an academic inclination; thus it lacks sufficient support for integration with industry standard production platforms. So, if you look for such hardware, embedded, or software applications, you should consider using some other or additional tools.

LabView is one such example, and its native integration with NI produced DSP chips is probably its strongest part. Yet for core algorithm development, (where you need most flexibility), you would still require an assistance from MATLAB or its free limited-clone OCTAVE.

Python is a free, expanding, and versatile language. You can find applications from AI research down to some embedded projects, through its package-library support. I don't like its syntax which simply sucks. Neverthless it is free, growing, and academically accepted; and it comes with excellent Linux (vs poor Windows) support. Its packages, (such as machine learning and AI), are probably its strongest parts, and syntax is the weakest.

OpenCV is a specialised tool for unique task of image processing algorithm and program development for indutrial targets. It's quite popular in machine vision, or industrial automation world. I think however that, as Python packages are growing and improving day by day, they may eventually replace whole OpenCV as a package of Python in the future.

MATLAB's Image Processing toolbox is much richer than what you'd find on Python except OpenCV.OpenCV on Python doesn't feel natural yet still give you access to basically the largest library of image processing related algorithms. Usually they are also implemented quite efficiently.

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