Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing

Talking with hands and feet: Selective somatosensory attention and fMRI enable robust and convenient brain-based communication

In brain-based communication, voluntarily modulated brain signals (instead of motor output) are utilized to interact with the outside world. The possibility to circumvent the motor system constitutes an important alternative option for severely paralyzed. Most communication brain-computer interface (BCI) paradigms require intact visual capabilities and impose a high cognitive load, but for some patients, these requirements are not given. In these situations, a better-suited, less cognitively demanding information-encoding approach may exploit auditorily-cued selective somatosensory attention to vibrotactile stimulation. Here, we propose, validate and optimize a novel communication-BCI paradigm using differential fMRI activation patterns evoked by selective somatosensory attention to tactile stimulation of the right hand or left foot. Using cytoarchitectonic probability maps and multi-voxel pattern analysis (MVPA), we show that the locus of selective somatosensory attention can be decoded from fMRI-signal patterns in (especially primary) somatosensory cortex with high accuracy and reliability, with the highest classification accuracy (85.93%) achieved when using Brodmann area 2 (SI-BA2) at a probability level of 0.2. Based on this outcome, we developed and validated a novel somatosensory attention-based yes/no communication procedure and demonstrated its high effectiveness even when using only a limited amount of (MVPA) training data. For the BCI user, the paradigm is straightforward, eye-independent, and requires only limited cognitive functioning. In addition, it is BCI-operator friendly given its objective and expertise-independent procedure. For these reasons, our novel communication paradigm has high potential for clinical applications.

S1 represents multisensory contexts and somatotopic locations within and outside the bounds of the cortical homunculus

Recent literature suggests that tactile events are represented in the primary somatosensory cortex (S1) beyond its long-established topography; in addition, the extent to which S1 is modulated by vision remains unclear. To better characterize S1, human electrophysiological data were recorded during touches to the forearm or finger. Conditions included visually observed physical touches, physical touches without vision, and visual touches without physical contact. Two major findings emerge from this dataset. First, vision strongly modulates S1 area 1, but only if there is a physical element to the touch, suggesting that passive touch observation is insufficient to elicit neural responses. Second, despite recording in a putative arm area of S1, neural activity represents both arm and finger stimuli during physical touches. Arm touches are encoded more strongly and specifically, supporting the idea that S1 encodes tactile events primarily through its topographic organization but also more generally, encompassing other areas of the body.

Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs

As a widely used brain–computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants’ mental fatigue. The study’s results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants’ mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users’ mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.

Lower Limb Exoskeleton Sensors: State-of-the-Art

Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human-machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments.

Progress in Brain Computer Interface: Challenges and Opportunities

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Somatotopic Arrangement of the Human Primary Somatosensory Cortex Derived From Functional Magnetic Resonance Imaging

Functional magnetic resonance imaging (fMRI) was used to estimate neuronal activity in the primary somatosensory cortex of six participants undergoing cutaneous tactile stimulation on skin areas spread across the entire body. Differences between the accepted somatotopic maps derived from Penfield's work and those generated by this fMRI study were sought, including representational transpositions or replications across the cortex. MR-safe pneumatic devices mimicking the action of a Wartenberg wheel supplied touch stimuli in eight areas. Seven were on the left side of the body: foot, lower, and upper leg, trunk beneath ribcage, anterior forearm, middle fingertip, and neck above the collarbone. The eighth area was the glabella. Activation magnitude was estimated as the maximum cross-correlation coefficient at a certain phase shift between ideal time series and measured blood oxygen level dependent (BOLD) time courses on the cortical surface. Maximally correlated clusters associated with each cutaneous area were calculated, and cortical magnification factors were estimated. Activity correlated to lower limb stimulation was observed in the paracentral lobule and superomedial postcentral region. Correlations to upper extremity stimulation were observed in the postcentral area adjacent to the motor hand knob. Activity correlated to trunk, face and neck stimulation was localized in the superomedial one-third of the postcentral region, which differed from Penfield's cortical homunculus.