Evaluation of Multi-layer Perceptron Neural Networks in Predicting Ankle Dorsiflexion in Healthy Adults using Movement-related Cortical Potentials for BCI-Neurofeedback Applications

Abstract

Brain computer interface (BCI) systems were initially developed to replace lost function; however, they are being increasingly utilized in rehabilitation to restore motor functioning after brain injury. In such BCI-mediated neurofeedback training (BCI-NFT), the brain-state associated with movement attempt or intention is used to activate an external device which assists the movement while providing sensory feedback to enhance neuroplasticity. A critical element in the success of BCI-NFT is accurate timing of the feedback within the active period of the brain state. The overarching goal of this work was to develop a reliable deep learning model that can predict motion before its onset, and thereby deliver the sensory stimuli in a timely manner for BCI-NFT applications. To this end, the main objective of the current study was to design and evaluate a Multi-layer Perceptron Neural Network (MLP-NN). Movement-related cortical potentials (MRCP) during planning and execution of ankle dorsiflexion was used to train the model to classify dorsiflexion planning vs. rest. The accuracy and reliability of the model was evaluated offline using data from eight healthy individuals (age: 26.3 ± 7.6 years). First, we evaluated three different epoching strategies for defining our 2 classes, to identify the one which best discriminated rest from dorsiflexion. The best model accuracy for predicting ankle dorsiflexion from EEG before movement execution was 84.7%. Second, the effect of various spatial filters on the model accuracy was evaluated, demonstrating that the spatial filtering had minimal effect on model accuracy and reliability.

Figure 1.

Subjects were seated in recumbent position in front of a monitor. They were instructed to dorsiflex when a graphical cue appeared on the monitor. 64-channel EEG was recorded simultaneously.

Figure 2.

The block diagram of the two analyses. Epoching analysis: where the effect of three time-epochings strategies for defining the rest and motion classes were evaluated. Spatial Filtering analysis: where the effect of commonly used spatial filtering techniques on the accuracy of the model were evaluated. Six combination of spatial filtering, i.e., six conditions (no-Filter, CAR, CSP, CAR+CSP, Lap and CAR+Lap), were applied to the Motor planning_2 and their accuracies were evaluated. Then the condition with the highest accuracy, no-Filter, were used for additional analysis, i.e., trial-based, subject based and individualized model evaluation.

Figure 3.

Effect of rest and motion execution/planning time periods on the classification accuracy. The standard deviations are [0.5,0.7,0.5], for the first, second and third bars, respectively. Motor Exe showed significantly higher accuracy than the other two datasets. The accuracy of Motor Plan2 dataset was significantly higher than Motor Plan.

Figure 4.

The effect of spatial filtering on MLP-NN classification accuracy. No-Filter shows the detection accuracy with no spatial filter. CAR shows the effect of re-referencing by Common Average Reference (CAR). CAR & Lap shows the effect of both re-referencing by CAR and applying the Laplacian filter. The standard deviations are [0.7,0.5, 0.6, 0.5,1.2,0.6] for the 1^st to 6^th bar, respectively. The accuracy of no-Filter dataset was significantly higher than those of CSP dataset and Laplacian dataset. The accuracy of CSP was significantly higher than Laplacian. Adding CAR did not changed the accuracies significantly.

Figure 5.

The mean true positive vs false positive rate of spatial filtering across five-fold validation.