# Neural Encoding of Pavement Textures during Exoskeleton Control: A Pilot Study
## 1 Introduction
Subjects were tested in four different pavements (flat, carpet, foam and rubber circles) with and without the exoskeleton. To simulate the frequency and sound of the Exoskeleton condition, during the No Exoskeleton condition, the subjects’ movements were reproduced by an external operator controlling an empty exoskeleton. ANN models are inspired by the functioning of the human brain, more specifically, by how the neurons communicate between them. The basic unit of the ANN is the artificial neuron, which is also designated as the perceptron (neural network unit). The basic functioning of these units consists of input values that are multiplied by a specific weight. Afterwards, these values are summed, and this weighted sum is then passed to an activation function that controls the output, since it is responsible for concluding if the weighted sum value is smaller or bigger than the threshold value [15; 16; 17]. Several other types of classifiers based in tensor methods, deep learning, and Riemannian geometry are now gaining importance due to their ability to be used with smaller training data sets, their higher signal-to-noise ratios, and their capability for transfer learning [14; 15]. In this study, we conducted a preliminary investigation to address the following questions: (1) whether there were clearly discernible patterns of neural activity recorded from electrodes at the C3 and C4 positions when subjects wore an exoskeleton, (2) whether neural activity exhibited variations when subjects walked on different textured surfaces, and (3) how four commonly used classifiers (SVM, KNN, LDA, and ANN) performed in the classification of exoskeleton use (i.e., Exoskeleton/No Exoskeleton) on four distinct types of pavements (flat surface, carpet, foam, and rubber circles). We formulated the following hypotheses for this study: Hypothesis (1) proposed that the use of an exoskeleton would lead to changes in the power of specific frequency bands, as it has been demonstrated that exoskeleton control can interfere with other brain functions [18; 19]. Hypothesis (2) proposed that stepping on less stable textures, which require greater stability during walking, would result in an increase in the central (C3, C4) alpha and theta frequency bands [20]. These frequency bands are typically associated with tasks involving the vestibular system. Lastly, Hypothesis (3) proposed that all four classifiers would demonstrate the ability to classify the eight-class problem (with/without Exoskeleton for all four pavements) above a chance level based solely on neural activity. To gather preliminary evidence for these hypotheses, we conducted experiments that involved five subjects. The subjects performed walking tasks with and without an exoskeleton on four different textured pavements: flat (representing regular ground), carpet, foam, and rubber circles (refer to Figure 1). During the experiments, the subjects' neural activity was recorded using an electroencephalogram (EEG). When the subjects were not using the exoskeleton, a member of the research team (referred to as exoskeleton operator) replicated the subjects' movements with the exoskeleton. This was done by holding the exoskeleton near the subject, ensuring that the frequency and sounds associated with the exoskeleton's movements remained similar between the measurements with and without the exoskeleton. (see Figure 1; also see Materials and Methodsbelow). The developed work provides information on the neurophysiological basis of tactile processing during exoskeleton control. Additionally, this study also offers relevant data for the development of tactile-sensation-based BMI systems that aim to provide sensory feedback to their users. The document is organized into five different chapters. The current Section 1 intends to describe the significance of this work and its relevance to the scientific community. Section 2 describes the methods used in this work in order to acquire, process, and classify the biosignal.