Abstract

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome.

Highlights

  • convolutional neural network (CNN)-like architecture was designed with aafirst main goal of achieving achievingthe themaximization maximizationofofthe the information translate rate (ITR), while ture was designed with first main goal of ITR, while ture was designed with a first main goal of achieving the maximization of the ITR, while keeping goal, we investigatedthe thefull full implementakeepinghigh highrecognition recognitionaccuracy

  • A single-trial P300 detector that combines a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN), maximizing the accuracy in single-trial P300 detection, for a low number of stimuli repetition, enhancing—as a consequence—the brain–computer interface (BCI) speed in terms ITR and ensuring the full compatibility with microcontrollers implementation, has been presented

  • The symbolized EEG signals were sent to an autoencoder model to emphasize those temporal features that can be well comprised by the following sequential network composed of seven layers

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Summary

Introduction

We propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes.

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