On the Use of <i>t</i>-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease

<div>Typical example of preprocessed signals. Results of the application of the windowing and filtering steps described in [<a href="/journals/cmmm/2018/8019232/#B20">20</a>]. The distinct tasks (T1, T2, T3, and T4) are separated by pulses.</div>

Computational and Mathematical Methods in Medicine

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Figure 2: On the Use of <i>t</i>-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease 

Figure 2 | On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease 