## 1 Introduction  
Approximately 50% of stroke survivors have reported persistent sensory deficiencies for both somatosensation and proprioception [1; 2]. For example, they often have difficulties in perceiving pain, temperature, pressure, posture, and light touch [3]. Sensory deficiencies have profound negative impacts on the functional ability and independency in daily living, which further affect motor recovery after stroke [4; 5]. Fine tactile sensation is an elementary somatosensory function for obtaining external information through touch [6]. Previous studies have shown that fine tactile sensation also provide valid spatial references for body positions to reduce postural sway [7], and it may act as an indicator to enhance sensory feedback in position control [8; 9]. However, rehabilitation for sensory functions has been overlooked in the traditional practices, when compared with efforts for motor restoration; this is attributed to the lack of effective evaluation measures for sensory impairments [10]. Objective and efficient assessments of sensory impairments are important for long-term post-stroke rehabilitation with repeated measurements during follow up [11]. However, subjective and manual measurements have been used traditionally for sensory impairment assessments [12]. For example, the Fugl-Meyer assessment (FMA) [13] and Semmes-Weinstein monofilament test [14] are commonly used in current evaluations of fine tactile sensations because of the ease of interpretation of the assessment results. Additionally, the measurement process highly relies on the personal experiences of the assessor, where achieving consistency in measurements is challenging when the stroke population increases during long-term service [15]. Neuroimaging techniques have been introduced to provide objective data for sensory impairment assessments [16]. The common neuroimaging techniques include functional magnetic resonance imaging (fMRI), positron emission tomography (PET), neuromolecular imaging, and electroencephalography (EEG), among others [17; 18; 19; 20; 21; 22; 23]. These approaches characterize neural circuitry changes during post-stroke sensorimotor recovery; however, such medical equipment is expensive and the preparations before neuroimaging-based examinations are complicated compared to the traditional clinical assessments [24]. Among these techniques, owing to the advantage of high temporal resolution, EEG has been applied to detect transient sensory neural responses during fine tactile stimulations [25; 26]. For example, [PERSON] et al. compared the effects of different tactile exploration tasks, i.e., passively or actively moving a tactile board, on post-stroke brain activation using EEG [27]. The sensory motor rhythm indicated by the EEG relative powers from the right prefrontal and parietal lobes during active tactile perceptions were significantly greater than those in the damaged left hemisphere during passive tactile perception [27]. In our previous work [28], post-stroke sensory impairment of fine tactile sensation was measured quantitatively via EEG during textile fabric stimulation, i.e., simulation of the common fabric-skin touch. We observed EEG relative spectral power (RSP) differences after stroke, i.e., RSP intensities in different frequency bands between unimpaired and stroke populations [28]. However, neuroimaging-based measurements usually generate large amounts of data, whose interpretations still heavily rely on human professionals, which is time consuming and labor demanding [29; 30]. Neuroimaging data interpretation by machine-learning (ML) techniques has been a promising approach to reduce manpower workload in data interpretations [31]. ML is a technique that can help develop an automatic predictive model by learning the relationships between features and targets from a given set of historical data before application to repeated analyses on massive data [32].