# Neural Encoding of Pavement Textures during Exoskeleton Control: A Pilot Study
## 1 Introduction  
Powered exoskeletons are becoming widely spread and reliable tools in the field of neurorehabilitation for spinal cord injuries, with multiple studies highlighting the beneficial neural plastic effects potentiated through their use. Previous studies have demonstrated that neurorehabilitation protocols combining motor-imagery-based brain-machine interfaces (BMIs) for exoskeleton control have led to significant neurological improvements [1, 2, 3]. Specifically, the combination of tactile feedback with motor imagery is becoming more common, thereby emphasizing the relevance of tactile processing for neurorehabilitation. Currently, the utilization of an exoskeleton (refer to Figure 1) is often associated with the use of bulky equipment, which often necessitates close contact between the exoskeleton and the soles of the feet. This contact is achieved through artificial soles that provide support to the lower limbs. However, the presence of an additional component betweenthe foot sole and the ground has the potential to interfere with the processing of the somatosensory information related to textures. Therefore, BMIs that integrate exoskeletons and somatosensory processing can offer numerous advantages, but the use of exoskeletons can have two potential effects: (i) they may reduce the number of somatosensory signals transmitted to the spinal cord, and (ii) they may introduce additional changes to the dynamics of somatosensory processing. Both of these effects have the potential to alter the performance of BMIs. Considering the possibility of diminished differences in somatosensory neural activity due to the aforementioned factors, it is important to evaluate the following: (a) the general patterns of neural activity associated with exoskeleton use and (b) the performance of common classifiers decoding the neural activity of individuals controlling an exoskeleton when encountering different textured pavements. Neural correlates of tactile sensory processing have been extensively studied using electroencephalography [4; 5; 6; 7], but there has been a growing interest in complementing the study of neural signals with the use of classifiers [8; 9; 10; 11; 12; 13; 14; 15]. Some of the most popular classifiers for neural decoding include the linear support vector machine (L-SVM), the K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), and the artificial neural network (ANN) [12; 13; 14]. The SVM creates a separation hyperplane to separate the two different input classes into the appropriate category. The data points nearest to the hyperplane are designated by support vectors, and the classification process is based on the mapping of the new input data point. This model evaluates on which side of the hyperplane the new value is located, which it then attributes to the corresponding class according to nearest support vector. This algorithm possesses lower computational complexity when compared with other supervised algorithms, such as the ANN and KNN [15; 16; 17]. The KNN is a nonparametric supervised algorithm that classifies different objects based on their K-closest neighbors. Therefore, for a new data point, this model determines the class by calculating the majority of the neighbours class labels. The letter \"K\" identifies the number of chosen neighbours, and, consequently, the selected value plays an important part in the model's performance [15; 16; 17]. LDA is classified as a linear technique that uses, as with the SVM model, a hyperplane to separate the different classes and to classify the input data values. The main goal of this model is to reduce the input value space while maximizing the separation between classes in the data. This classifier has a low computational cost [15; 16; 17]. Figure 1: Workflow diagram. The study included four different aspects: behavioral testing in experimental conditions, the recording of EEG signals, feature extraction, and classification.