As enhanced FFR reproduces features of the stimuli with more fidelity, the decoding accuracy of the classifier can be used as a dependant measure to reveal experience-dependant effects in certain populations (Llanos et al., 2017 2019). This information is useful to validate experimental procedures, adjust research designs and target research questions. 13 non-musicians).Īs the equipment used to collect FFRs and population targeted can influence the quality of the signal, this script also tracks the number of FFR trials required to obtain certain levels of decoding accuracy. These procedures were done on 26 participants (13 musicians vs. The machine-learning classifier targeted in this tutorial, the Hidden Markov Model, aims to classify if the EEG-FFRs of each participants were generated by a stimulus that was either a speech sound or a piano tone of the same fundamental frequency (98 Hz) and duration (100ms).
We aim to buid from this structure and repository! Hidden Markov Model - Classification Goal:
How to build a hidden markov model matlab free#
Feel free to contribute to it or indicate issues with the link below. Hence, I tried to implement an open approach in this ML tutorial. Nevertheless, there are many open science tools that are compatible with matlab scripts and files. Thus, at the current moment, it would be extremely difficult to study FFR without using MATLAB. Specialized fields, such as FFR research, have the wide majority of their resources, expertise, and tools in matlab.
How to build a hidden markov model matlab software#
Is it mandatory to abandon MATLAB to have open science practices? Matlab may be a commercial software, but it would be sad to limit the open science movement to open source software users. Notes on Open Science Practices and Matlab I aim to put together a set of resources that would be accessible to my research assistants, collaborators (who also have students to train on these ML procedures), other FFR researchers, and my futur self! Thus, the data of this tutorial will be in format that align with components of standards in project management (EEG-BIDS), use jupyter notebooks implemented in a virtual environement (allowing the use MATLAB scripts), and use basic matlab vizualization tools to generate figures of the results. for other classifiers such as a Support Vector Machine (SVM), Cross-correlation (XCorr), LSTM). As I am novice with coding, I aim to implement a classifier that I use repeatively in my doctoral research (an Hidden Markov Model ) into a structure that would provide some flexibility for future usages (with other variables or datasets), and would allow to build extensions (e.g. This tutorial first aim is to provide a reproducible workflow, that is beginner-friendly, with machine-learning (ML) procedures written in MATLAB.
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