Rodrigo Ramele

Rodrigo Ramele

Doctor in Engineering in Brain Computer Interfaces

Posts by Collection

portfolio

Equilivest

A Robotic Vest to aid in Post-Stroke Dynamic Human Balance Rehabilitation

ALPIBot

Mobile Robotic Platform to carry the Oxygen Tank

KComplex

KComplex and Slow Wave Localization and Detection for EEG Sleep Research

JS

Robotic platform to study young toddlers behaviour

publications

BCI classification based on signal plots and SIFT descriptors

Published in 2016 4th International Winter Conference on Brain-Computer Interface, 2015

Brain Computer Interfaces are a challenging technology with amazing prospects but its push into mainstream assistive applications has not arrived yet. In this work a new method to analyze and classify EEG, Electroencefalography, signals, is proposed which is based on the extraction of visually relevant feature descriptors from images of the signal plots. This procedure has the advantage that the features which are used to classify are visually relevant and meaningful to a human observer, particularly to a physician, improving close collaboration and clinical adoption. Moreover, this may allow to tackle this demanding technology from a different perspective and improve the prospects of the BNCI, Brain/Neural Computer Interaction field.

Recommended citation: Ramele, R., Villar A.J., and Santos J.M., "A Brain Computer Interface Classification Method Based on Signal Plots." 4th Winter Conference on Brain Computer Interfaces, Yongpyong, Korea, February 2015. IEEE Signal and Processing, 2016. https://ieeexplore.ieee.org/abstract/document/7457454

EEG waveform analysis of P300 ERP with applications to brain computer interfaces

Published in Brain Science, 2018

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

Recommended citation: Ramele, R. and Villar A.J. and Santos J.M., ”EEG Waveform Analysis of P300 ERP with applications to Brain Computer Interfaces”, MDPI Brain Sciences Journal, Special Issue:”Brain-Computer Interfaces for Human Augmentation”,2018, 8(11), 199. https://www.mdpi.com/2076-3425/8/11/199

Histogram of Gradient Orientations of Signal Plots Applied to P300 Detection

Published in Frontier in Computational Intelligence, 2019

The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects.

Recommended citation: Ramele R, Villar AJ and Santos JM (2019) Histogram of Gradient Orientations of Signal Plots Applied to P300 Detection. Front. Comput. Neurosci. 13:43. doi: 10.3389/fncom. 2019.00043 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624778/

talks

teaching

Scientific Data Science

Graduate course, ITBA University, Data Science Specialization, 2019

Time Series Analysis. Scientific Python. Spectral Filters. Spatial Filters. Segmentation. Time Series Features. Feature Engineering. Neuro inspiration to Neural Networks. Deep Learning. Classification, Regression and Generation.

Artificial Intelligence Systems

Undergraduate Course, ITBA University, Computer Engineering Department, 2020

Introduction to Artificial Intelligence. Definition of Intelligence. A*. Genetic Algorithms. Perceptron. Multilayer Perceptron. Optimization. PCA. Cross-Validation and Performance Metrics. Oja and Sanjer. Deep Learning. Autoencoders. Variational Autoencoders. GANs. CNN.