Abstract
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.
Highlights
Recent advances in neuroimagining techniques, radio-nuclear and radiological scanning methods (Schomer and Silva, 2010), have diminished the prospects of the traditional Electroencephalography, the advent and development of digitized devices has impelled for a revamping of this hundred years old technology
This paper reports a method to: (1) describe a procedure to capture the shape of a waveform of an Event-Related Potentials (ERP) component, the P300, using histograms of gradient orientations extracted from images of signal plots, and (2) outline the way in which this procedure can be used to implement an P300-Based Brain Computer Interfaces (BCI)
It is verified for the dataset of Amyotrophic Lateral Sclerosis (ALS) patients that it has similar performance against other methods like Stepwise Linear Discriminant Analysis (SWLDA) or Support Vector Machine (SVM), which use a multichannel feature (Quade test with p = 0.55) whereas for the dataset of healthy subjects significant differences are found (Quade test with p = 0.02) where only the Histogram of Gradient Orientations (HIST) method achieves a different performance than SVM
Summary
Recent advances in neuroimagining techniques, radio-nuclear and radiological scanning methods (Schomer and Silva, 2010), have diminished the prospects of the traditional Electroencephalography, the advent and development of digitized devices has impelled for a revamping of this hundred years old technology. Their versatility, ease of use, temporal resolution, ease of development and production, and its proliferation as consumer devices, are pushing EEG to become the de-facto non invasive portable or ambulatory method to access and harness brain information (De Vos and Debener, 2014). The holly grail of BCI is to implement a new complete and alternative pathway to restore lost locomotion (Wolpaw and Wolpaw, 2012)
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