Steady-state Somatosensory Evoked Potentials Research Articles

High-Frequency Vibrating Stimuli Using the Low-Cost Coin-Type Motors for SSSEP-Based BCI.

Steady-state somatosensory-evoked potential- (SSSEP-) based brain-computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long-time training. Despite the advancement of various noninvasive electroencephalogram- (EEG-) based BCI paradigms, researches on SSSEP with the various frequency range and related classification algorithms are relatively unsettled. In this study, we investigated the feasibility of classifying the SSSEP within high-frequency vibration stimuli induced by a versatile coin-type eccentric rotating mass (ERM) motor. Seven healthy subjects performed selective attention (SA) tasks with vibration stimuli attached to the left and right index fingers. Three EEG feature extraction methods, followed by a support vector machine (SVM) classifier, have been tested: common spatial pattern (CSP), filter-bank CSP (FBCSP), and mutual information-based best individual feature (MIBIF) selection after the FBCSP. Consequently, the FBCSP showed the highest performance at 71.5 ± 2.5% for classifying the left and right-hand SA tasks than the other two methods (i.e., CSP and FBCSP-MIBIF). Based on our findings and approach, the high-frequency vibration stimuli using low-cost coin motors with the FBCSP-based feature selection can be potentially applied to developing practical SSSEP-based BCI systems.

Individual Optimal Attentional Strategy in Motor Learning Tasks Characterized by Steady-State Somatosensory and Visual Evoked Potentials.

Focus of attention is one of the most influential factors facilitating motor performance. Previous evidence supports that the external focus (EF) strategy, which directs attention to movement outcomes, is associated with better motor performance than the internal focus (IF) strategy, which directs attention to body movements. However, recent studies have reported that the EF strategy is not effective for some individuals. Furthermore, neuroimaging studies have demonstrated that the frontal and parietal areas characterize individual optimal attentional strategies for motor tasks. However, whether the sensory cortices are also functionally related to individual optimal attentional strategy remains unclear. Therefore, the present study examined whether an individual’s sensory processing ability would reflect the optimal attentional strategy. To address this point, we explored the relationship between responses in the early sensory cortex and individuals’ optimal attentional strategy by recording steady-state somatosensory evoked potentials (SSSEP) and steady-state visual evoked potentials (SSVEP). Twenty-six healthy young participants first performed a motor learning task with reaching movements under IF and EF conditions. Of the total sample, 12 individuals showed higher after-effects under the IF condition than the EF condition (IF-dominant group), whereas the remaining individuals showed the opposite trend (EF-dominant group). Subsequently, we measured SSSEP from bilateral primary somatosensory cortices while presenting vibrotactile stimuli and measured SSVEP from bilateral primary visual cortices while presenting checkerboard visual stimuli. The degree of increasing SSSEP response when the individuals in the IF-dominant group directed attention to vibrotactile stimuli was significantly more potent than those in the EF-dominant individuals. By contrast, the individuals in the EF-dominant group showed a significantly larger SSVEP increase while they directed attention to visual stimuli compared with the IF-dominant individuals. Furthermore, a significant correlation was observed such that individuals with more robust IF dominance showed more pronounced SSSEP attention modulation. These results suggest that the early sensory areas have crucial brain dynamics to characterize an individual’s optimal attentional strategy during motor tasks. The response characteristics may reflect the individual sensory processing ability, such as control of priority to the sensory inputs. Considering individual cognitive traits based on the suitable attentional strategy could enhance adaptability in motor tasks.

Research on feature classification of lower limb motion imagination based on electrical stimulation to enhance rehabilitation

Motor imaging therapy is of great significance to the rehabilitation of patients with stroke or motor dysfunction, but there are few studies on lower limb motor imagination. When electrical stimulation is applied to the posterior tibial nerve of the ankle, the steady-state somatosensory evoked potentials (SSSEP) can be induced at the electrical stimulation frequency. In order to better realize the classification of lower extremity motor imagination, improve the classification effect, and enrich the instruction set of lower extremity motor imagination, this paper designs two experimental paradigms: Motor imaging (MI) paradigm and Hybrid paradigm. The Hybrid paradigm contains electrical stimulation assistance. Ten healthy college students were recruited to complete the unilateral movement imagination task of left and right foot in two paradigms. Through time-frequency analysis and classification accuracy analysis, it is found that compared with MI paradigm, Hybrid paradigm could get obvious SSSEP and ERD features. The average classification accuracy of subjects in the Hybrid paradigm was 78.61%, which was obviously higher than the MI paradigm. It proves that electrical stimulation has a positive role in promoting the classification training of lower limb motor imagination.

Inter-stimulus phase coherence in steady-state somatosensory evoked potentials and its application in improving the performance of single-channel MI-BCI

Objective. With the development of clinical applications of motor imagery-based brain–computer interfaces (MI-BCIs), a single-channel MI-BCI system that can be easily assembled is an attractive goal. However, due to the low quality of the spectral power features in the traditional MI-BCI paradigm, the recognition performance of current single-channel systems is far lower than that of multi-channel systems, impeding their use in clinical applications. Approach. In this study, the subjects’ right and left hands were stimulated simultaneously at different frequencies to induce steady-state somatosensory evoked potentials (SSSEP). Subjects then performed motor imagery (MI) tasks. A new electroencephalography (EEG) index, inter-stimulus phase coherence (ISPC), was built to measure phase desynchronization of SSSEP caused by MI. Then, ISPC is introduced as a feature into left-hand and right-hand MI recognition. Main results. ISPC analysis found that left-handed MI can cause a significant decrease in phase synchronization in contralateral sensorimotor SSSEP, while right-handed MI has little effect on it, and vice versa. Combining ISPC features with traditional spectral power features, the single-channel left-hand versus right-hand MI recognition accuracy reaches 81.0%, which is much higher than that observed with traditional MI paradigms (about 60%). Significance. This work shows that the hybrid MI-SSSEP paradigm can provide more sensitive EEG features to decode motor intentions, demonstrating its potential for clinical applications.

Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery.

In recent years, the brain-computer interface (BCI) based on motor imagery (MI) has been considered as a potential post-stroke rehabilitation technology. However, the recognition of MI relies on the event-related desynchronization (ERD) feature, which has poor task specificity. Further, there is the problem of false triggering (irrelevant mental activities recognized as the MI of the target limb). In this paper, we discuss the feasibility of reducing the false triggering rate using a novel paradigm, in which the steady-state somatosensory evoked potential (SSSEP) is combined with the MI (MI-SSSEP). Data from the target (right hand MI) and nontarget task (rest) were used to establish the recognition model, and three kinds of interference tasks were used to test the false triggering performance. In the MI-SSSEP paradigm, ERD and SSSEP features modulated by MI could be used for recognition, while in the MI paradigm, only ERD features could be used. The results showed that the false triggering rate of interference tasks with SSSEP features was reduced to 29.3%, which was far lower than the 55.5% seen under the MI paradigm with ERD features. Moreover, in the MI-SSSEP paradigm, the recognition rate of the target and nontarget task was also significantly improved. Further analysis showed that the specificity of SSSEP was significantly higher than that of ERD (p < 0.05), but the sensitivity was not significantly different. These results indicated that SSSEP modulated by MI could more specifically decode the target task MI, and thereby may have potential in achieving more accurate rehabilitation training.

Electrical stimulation-induced SSSEP as an objective index to evaluate the difference of tactile acuity between the left and right hand

Objective. The objective of this study is to propose an objective index to evaluate the difference of tactile acuity between the left and right hand based on steady-state somatosensory evoked potential (SSSEP). Approach. Two kinds of tactile sensations (vibration and pressure) with three levels of intensities (low/medium/high) were evoked on two finger areas of the left or right hand (thumb and index for healthy hands, thumb and index-projected areas for disabled hands) via transcutaneous electrical nerve stimulation (TENS). Three forearm amputees and 13 able-bodied subjects were recruited to discriminate the specific level and area of the applied stimulation. Electroencephalography was adopted to simultaneously record the somatosensory cortex response to TENS. We assessed the discrimination performance (discrimination accuracy rate (AR) and response time (RT)) to quantify the tactile acuity, while the evoked SSSEP was synchronously analyzed. Linear regression analyses were performed between the difference of SSSEP amplitudes and the difference of discrimination performance for the left and right hand stimulation. Main results. Frequency domain analysis revealed that SSSEP amplitude increased with the increase of the stimulation intensity. There were positive correlations between the difference of SSSEP amplitudes and the difference of ARs for the left and right hand stimulation in the sensations of vibration (R2 = 0.6389 for able-bodied subjects, R2 = 0.5328 for amputees) and pressure (R2 = 0.6102 for able-bodied subjects, R2 = 0.5452 for amputees), respectively. Significance The SSSEP amplitude could be used as an objective index to evaluate the difference of the tactile acuity between the left and right hand and has the potential to be applied in sensory rehabilitation for amputees or stroke patients.

Classification of Selective Attention Within Steady-State Somatosensory Evoked Potentials From Dry Electrodes Using Mutual Information-Based Spatio-Spectral Feature Selection

Nowadays, the steady-state somatosensory evoked potential (SSSEP)-based brain-computer interfaces (BCIs) has been developed for improving the quality of daily life for people with physical disabilities. However, due to its poor performance of recognizing selective attention tasks and inattention(rest)-state, the SSSEP-based BCI has not been widely used for practical interfaces. In this paper, we propose a mutual information-based spatio-spectral feature selection method for recognizing selective attention tasks and inattention(rest)-state using dry electrodes considering a real-life application, when vibration stimuli were applied to both index fingers. In our methods, the filter-bank common spatial pattern (FBCSP) was used for extracting spatio-spectral features of the SSSEP. Then, discriminative features were selected using a mutual information-based best individual feature (MIBIF) algorithm. The regularized linear discriminant analysis (RLDA) used as the classifier. The feasibility of the proposed method was demonstrated through eight healthy subjects using the vibration stimuli induced SSSEP with spatially clear and distinguishable patterns for SSSEP-based BCI. From our study, the proposed method showed the best classification accuracy with a kappa value of 0.35±0.17. Furthermore, based on the ANOVA with posthoc tests, the proposed method showed significantly higher accuracy as 57.9% in decoding three classes (p -value <; 0.01) compared to the fast Fourier transform (FFT) and common spatial pattern (CSP)-based previous feature extraction methods. Consequently, the proposed FBCSP and MIBIF-based methods and findings can further help to improve decoding performance and develop the SSSEP-based BCI systems for real-world applications.

Extraction and Analysis of Steady State Somatosensory Evoked Potentials in Lower Limbs

Steady state somatosensory evoked potential (SSSEP) is an electrical signal generated in the cerebral cortex under external auxiliary stimulation. In the current literature, there are few studies on the extraction method of SSSEP in lower limbs. In order to be able to extract an effective SSSEP from lower limbs, electrical stimulation with different intensity is executed on the posterior tibial nerve of inner ankle of lower limbs. Select the optimal stimulation frequency combination among 5 stimulation frequency combinations through time-frequency analysis and brain topographic map. The results show that the electrical stimulation intensity of lower limbs is generally about 8 mA~25 mA, and the stimulation intensity of the left and right leg is different. The optimal stimulation frequency combination of the lower limbs is 28 Hz-33 Hz, with the best spectral characteristics for the subject at this frequency combination.

Visual object separation resolves competitive interactions in somatosensory cortex evoked signals during concurrent vibrotactile stimulation of the left and right hand

We investigated the effect of visual separation of left and right hemispaces within peripersonal space to sustained tactile spatial attention processing and showed that visually perceived object separation weakened the competitive interaction for concurrent vibrotactile stimulation to two hands. Participants received concurrent vibrotactile stimulation at different frequencies on two hands (right: 20Hz; left: 24Hz) for 4500ms to elicit steady-state somatosensory evoked potentials (SSSEPs). They attended to one of the stimulated hands and reacted to rare transient events embedded in the attended ongoing stream. Behavioral and neurophysiological results reveal that separation of spatial representation of tactile event locations could facilitate bilateral tactile spatial selection in sustained attention processing. Visual separation by a gap between hands increased the magnitude of attentional gain in SSSEPs compared to the situation when no gap was present. Results suggest that visual separation resolved the competition between concurrent tactile stimulation on two hands.

Enhancing performance of a motor imagery based brain–computer interface by incorporating electrical stimulation-induced SSSEP

Objective. We proposed a novel simultaneous hybrid brain–computer interface (BCI) by incorporating electrical stimulation into a motor imagery (MI) based BCI system. The goal of this study was to enhance the overall performance of an MI-based BCI. In addition, the brain oscillatory pattern in the hybrid task was also investigated. Approach. 64-channel electroencephalographic (EEG) data were recorded during MI, selective attention (SA) and hybrid tasks in fourteen healthy subjects. In the hybrid task, subjects performed MI with electrical stimulation which was applied to bilateral median nerve on wrists simultaneously. Main results. The hybrid task clearly presented additional steady-state somatosensory evoked potential (SSSEP) induced by electrical stimulation with MI-induced event-related desynchronization (ERD). By combining ERD and SSSEP features, the performance in the hybrid task was significantly better than in both MI and SA tasks, achieving a ~14% improvement in total relative to the MI task alone and reaching ~89% in mean classification accuracy. On the contrary, there was no significant enhancement obtained in performance while separate ERD feature was utilized in the hybrid task. In terms of the hybrid task, the performance using combined feature was significantly better than using separate ERD or SSSEP feature. Significance. The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.

A hybrid three-class brain–computer interface system utilizing SSSEPs and transient ERPs

Objective. This paper investigates the fusion of steady-state somatosensory evoked potentials (SSSEPs) and transient event-related potentials (tERPs), evoked through tactile simulation on the left and right-hand fingertips, in a three-class EEG based hybrid brain–computer interface. It was hypothesized, that fusing the input signals leads to higher classification rates than classifying tERP and SSSEP individually. Approach. Fourteen subjects participated in the studies, consisting of a screening paradigm to determine person dependent resonance-like frequencies and a subsequent online paradigm. The whole setup of the BCI system was based on open interfaces, following suggestions for a common implementation platform. During the online experiment, subjects were instructed to focus their attention on the stimulated fingertips as indicated by a visual cue. The recorded data were classified during runtime using a multi-class shrinkage LDA classifier and the outputs were fused together applying a posterior probability based fusion. Data were further analyzed offline, involving a combined classification of SSSEP and tERP features as a second fusion principle. The final results were tested for statistical significance applying a repeated measures ANOVA. Main results. A significant classification increase was achieved when fusing the results with a combined classification compared to performing an individual classification. Furthermore, the SSSEP classifier was significantly better in detecting a non-control state, whereas the tERP classifier was significantly better in detecting control states. Subjects who had a higher relative band power increase during the screening session also achieved significantly higher classification results than subjects with lower relative band power increase. Significance. It could be shown that utilizing SSSEP and tERP for hBCIs increases the classification accuracy and also that tERP and SSSEP are not classifying control- and non-control states with the same level of accuracy.

Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials.

In this work, we propose a novel brain-controlled wheelchair, one of the major applications of brain-machine interfaces (BMIs), that allows an individual with mobility impairments to perform daily living activities independently. Specifically, we propose to use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to tactile stimulation of specific frequencies, for a user's intention to control a wheelchair. In our system, a user had three possible commands by concentrating on one of three vibration stimuli, which were attached to the left-hand, right-hand, and right-foot, to selectively control the wheelchair. The three stimuli were associated with three wheelchair commands: turn-left, turn-right, and move-forward. From a machine learning perspective, we also devise a novel feature representation by combining spatial and spectral characteristics of brain signals. In order to validate the effectiveness of the proposed SSSEP-based system, we considered two different tasks: 1) a simple obstacle-avoidance task within a limited time and; 2) a driving task along the predefined trajectory of about 40 m length, where there were a narrow pathway, a door, and obstacles. In both experiments, we recruited 12 subjects and compared the average time of motor imagery (MI) and SSSEP-based controls to complete the task. With the SSSEP-based control, all subjects successfully completed the task without making any collision while four subjects failed it with MI-based control. It is also noteworthy that in terms of the average time to complete the task, the SSSEP-based control outperformed the MI-based control. In the other more challenging task, all subjects successfully reached the target location.

The Role of Transient Target Stimuli in a Steady-State Somatosensory Evoked Potential-Based Brain-Computer Interface Setup.

In earlier literature, so-called twitches were used to support a user in a steady-state somatosensory evoked potential (SSSEP) based brain–computer interface (BCI) to focus attention on the requested targets. Within this work, we investigate the impact of these transient target stimuli on SSSEPs in a real-life BCI setup. A hybrid BCI was designed which combines SSSEPs and P300 potentials evoked by twitches randomly embedded into the streams of tactile stimuli. The EEG of fourteen healthy subjects was recorded, while their left and right index fingers were simultaneously stimulated using frequencies selected in a screening procedure. The subjects were randomly instructed by a cue on a screen to focus attention on one or none of the fingers. Feature for SSSEPs and P300 potentials were extracted and classified using separately trained multi-class shrinkage LDA classifiers. Three-class classification accuracies significantly better than random could be reached by nine subjects using SSSEP features and by 12 subjects using P300 features respectively. The average classification accuracies were 48.6% using SSSEP and 50.7% using P300 features. By means of a Monte Carlo permutation test it could be shown that twitches have an attenuation effect on the SSSEP. Significant SSSEP blocking effects time-locked to twitch positions were found in seven subjects. Our findings suggest that the attempt to combine different types of stimulation signals like repetitive signals and twitches has a mutual influence on each other, which may be the main reason for the rather moderate BCI performance. This influence is originated at the level of stimulus generation but becomes apparent as physiological effect in the SSSEP. When designing a hybrid BCI based on SSSEPs and P300 potentials, one has to find an optimal tradeoff depending on the overall design goals or individual subjects' performance. Our results give therefore some new insights that may be useful for the successful design of hybrid BCIs.

Steady-State Somatosensory Evoked Potential for Brain-Computer Interface-Present and Future.

Brain-computer interface (BCI) performance has achieved continued improvement over recent decades, and sensorimotor rhythm-based BCIs that use motor function have been popular subjects of investigation. However, it remains problematic to introduce them to the public market because of their low reliability. As an alternative resolution to this issue, visual-based BCIs that use P300 or steady-state visually evoked potentials (SSVEPs) seem promising; however, the inherent visual fatigue that occurs with these BCIs may be unavoidable. For these reasons, steady-state somatosensory evoked potential (SSSEP) BCIs, which are based on tactile selective attention, have gained increasing attention recently. These may reduce the fatigue induced by visual attention and overcome the low reliability of motor activity. In this literature survey, recent findings on SSSEP and its methodological uses in BCI are reviewed. Further, existing limitations of SSSEP BCI and potential future directions for the technique are discussed.

Competitive interactions in somatosensory cortex for concurrent vibrotactile stimulation between and within hands

We investigated competitive neural interactions for processing resources during sustained spatial attention in somatosensation. Participants received concurrent vibrotactile stimulation at different frequencies to either non-adjacent left hand fingers (within-hand) or fingers on two hands (between-hands) for 4500ms to elicit steady-state somatosensory evoked potentials (SSSEPs). They attended to either one or both stimulated locations, respectively, and responded to rare events embedded in the ongoing stream. Behavioral and electrophysiological results revealed competitive interaction of simultaneous stimulation with overall smaller SSSEP amplitudes and poorer behavioral performance in the within-hand condition. Spatial proximity of stimulation did not affect the magnitude of attentional gain, it resulted in the occurrence of an intermodulation frequency indicating integrative stimulus processing in the within-hand condition. Obviously, while this integrative stimulus processing resulted in or was a result of greater intra-hemispheric competitive interactions, attention acted additively as a signal gain, which was independent of spatial competition.

Connections between intraparietal sulcus and a sensorimotor network underpin sustained tactile attention.

Previous studies on sustained tactile attention draw conclusions about underlying cortical networks by averaging over experimental conditions without considering attentional variance in single trials. This may have formed an imprecise picture of brain processes underpinning sustained tactile attention. In the present study, we simultaneously recorded EEG-fMRI and used modulations of steady-state somatosensory evoked potentials (SSSEPs) as a measure of attentional trial-by-trial variability. Therefore, frequency-tagged streams of vibrotactile stimulations were simultaneously presented to both index fingers. Human participants were cued to sustain attention to either the left or right finger stimulation and to press a button whenever they perceived a target pulse embedded in the to-be-attended stream. In-line with previous studies, a classical general linear model (GLM) analysis based on cued attention conditions revealed increased activity mainly in somatosensory and cerebellar regions. Yet, parametric modeling of the BOLD response using simultaneously recorded SSSEPs as a marker of attentional trial-by-trial variability quarried the intraparietal sulcus (IPS). The IPS in turn showed enhanced functional connectivity to a modality-unspecific attention network. However, this was only revealed on the basis of cued attention conditions in the classical GLM. By considering attentional variability as captured by SSSEPs, the IPS showed increased connectivity to a sensorimotor network, underpinning attentional selection processes between competing tactile stimuli and action choices (press a button or not). Thus, the current findings highlight the potential value by considering attentional variations in single trials and extend previous knowledge on the role of the IPS in tactile attention.

Achieving a hybrid brain–computer interface with tactile selective attention and motor imagery

Objective. We propose a new hybrid brain–computer interface (BCI) system that integrates two different EEG tasks: tactile selective attention (TSA) using a vibro-tactile stimulator on the left/right finger and motor imagery (MI) of left/right hand movement. Event-related desynchronization (ERD) from the MI task and steady-state somatosensory evoked potential (SSSEP) from the TSA task are retrieved and combined into two hybrid senses. Approach. One hybrid approach is to measure two tasks simultaneously; the features of each task are combined for testing. Another hybrid approach is to measure two tasks consecutively (TSA first and MI next) using only MI features. For comparison with the hybrid approaches, the TSA and MI tasks are measured independently. Main results. Using a total of 16 subject datasets, we analyzed the BCI classification performance for MI, TSA and two hybrid approaches in a comparative manner; we found that the consecutive hybrid approach outperformed the others, yielding about a 10% improvement in classification accuracy relative to MI alone. It is understood that TSA may play a crucial role as a prestimulus in that it helps to generate earlier ERD prior to MI and thus sustains ERD longer and to a stronger degree; this ERD may give more discriminative information than ERD in MI alone. Significance. Overall, our proposed consecutive hybrid approach is very promising for the development of advanced BCI systems.

Test–retest reliability of concurrently recorded steady-state and somatosensory evoked potentials in somatosensory sustained spatial attention

We investigated the test–retest reliability of sustained spatial attention modulation of steady-state somatosensory evoked potentials (SSSEPs) and the N140 component of the somatosensory evoked potentials (SEPs). Participants attended to one or both hands to perform a target detection task while concurrent mechanical vibrations were presented for 4500ms to both hands in two recording sessions. Results revealed that the amplitude and the attentional modulation of SSSEPs had high test–retest reliability, while the test–retest reliability for the N140 component was low. SSSEPs for stimuli with focused and divided attention had about the same amplitude. For the N140 component only the stimuli with focused attention were significantly enhanced. We found greater habituation effects for the N140 compared to SSSEP amplitudes but attentional modulation was unaffected in both signals. Given the great test–retest reliability of SSSEP amplitude modulation with attention, SSSEPs serve as an excellent tool for studying sustained spatial attention in somatosensation.

A Tactile Stimulation Device for EEG Measurements in Clinical Use

A tactile stimulation device for EEG measurements in clinical environments is proposed. The main purpose of the tactile stimulation device is to provide tactile stimulation to different parts of the body. To stimulate all four major types of mechanoreceptors, different stimulation patterns with frequencies in the range of 5-250 Hz have to be generated. The device provides two independent channels, delivers enough power to drive different types of electromagnetic transducers, is small and portable, and no expensive components are required to construct this device. The generated stimulation patterns are very stable, and deterministic control of the device is possible. To meet electrical safety requirements, the device was designed to be fully galvanically isolated. Leakage currents of the entire EEG measurement system including the tactile stimulation device were measured by the European Testing and Certifying Body for Medical Products Graz (Notified Body 0636). All measured currents were far below the maximum allowable currents defined in the safety standard EN 60601-1:2006 for medical electrical equipment. The successful operation of the tactile stimulation device was tested during an EEG experiment. The left and right wrist of one healthy subject were randomly stimulated with seven different frequencies. Steady-state somatosensory evoked potential (SSSEPs) could successfully be evoked and significant tuning curves at electrode positions contralateral to the stimulated wrist could be found. The device is ready to be used in clinical environment in a variety of applications to investigate the somatosensory system, in brain-computer interfaces (BCIs), or to provide tactile feedback.

Stability and distribution of steady-state somatosensory evoked potentials elicited by vibro-tactile stimulation

Steady-state somatosensory evoked potentials (SSSEPs) have been elicited applying vibro-tactile stimulation to all fingertips of the right hand. Nine healthy subjects participated in two sessions within this study. All fingers were stimulated 40 times with a 200-Hz carrier frequency modulated with a rectangular signal. The frequencies of the rectangular signal ranged between 17 and 35 Hz in 2 Hz steps. Relative band power tuning curves were calculated, introducing two different methods. Person-specific resonance-like frequencies were selected based on the data from the first session. The selected resonance-like frequencies were compared with the second session using an ANOVA for repeated measures to investigate the stability of SSSEPs over time. To determine, if SSSEPs can be classified with a classifier based on unseen data, an LDA classifier was trained with data from the first and applied to data from the second session. Person-specific resonance-like frequencies within a range from 19 to 29 Hz were found. The relative band power of the resonance-like frequencies did not differ significantly between the two sessions. Significant differences were found for the two methods and the used channels. SSSEPs were classified with a hit rate from 51 to 96 %.