Klaus Gramann

Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running

Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.

Electrocortical activity is coupled to gait cycle phase during treadmill walking

Recent findings suggest that human cortex is more active during steady-speed unperturbed locomotion than previously thought. However, techniques that have been used to image the brain during locomotion lack the temporal resolution necessary to assess intra-stride cortical dynamics. Our aim was to determine if electrocortical activity is coupled to gait cycle phase during steady-speed human walking. We used electroencephalography (EEG), motion capture, and a force-measuring treadmill to record brain and body dynamics while eight healthy young adult subjects walked on a treadmill. Infomax independent component analysis (ICA) parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach, and clustered electrocortical sources across subjects by similarities in dipole locations and power spectra. We then computed spectrograms for each electrocortical source that were time-locked to the gait cycle. Electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex exhibited significant (p<0.05) intra-stride changes in spectral power. During the end of stance, as the leading foot was contacting the ground and the trailing foot was pushing off, alpha- and beta-band spectral power increased in or near the left/right sensorimotor and dorsal anterior cingulate cortex. Power increases in the left/right sensorimotor cortex were more pronounced for contralateral limb push-off (ipsilateral heel-strike) than for ipsilateral limb push-off (contralateral heel-strike). Intra-stride high-gamma spectral power changes were evident in anterior cingulate, posterior parietal, and sensorimotor cortex. These data confirm cortical involvement in steady-speed human locomotion. Future applications of these techniques could provide critical insight into the neural mechanisms of movement disorders and gait rehabilitation.

Neural systems connecting interoceptive awareness and feelings.

In many theories of emotions the representations of bodily responses play an important role for subjective feelings. We tested the hypothesis that the perception of bodily states is positively related to the experienced intensity of feelings as well as to the activity of first‐order and second‐order brain structures involved in the processing of feelings. Using a heartbeat perception task, subjects were separated into groups with either high or poor interoceptive awareness. During emotional picture presentation we measured high‐density EEG and used spatiotemporal current density reconstruction to identify regions involved in both interoceptive awareness and emotion processing. We observed a positive relation between interoceptive awareness and the experienced intensity of emotions. Furthermore, the P300 amplitudes to pleasant and unpleasant pictures were enhanced for subjects with high interoceptive awareness. The source reconstruction revealed that interoceptive awareness is related to an enhanced activation in both first‐order structures (insula, somatosensory cortices) and second‐order structures (anterior cingulate, prefrontal cortices). We conclude that the perception of bodily states is a crucial determinant for the processing and the subjective experience of feelings. Hum. Brain Mapping, 2007.

Linking brain, mind and behavior.

Cortical brain areas and dynamics evolved to organize motor behavior in our three-dimensional environment also support more general human cognitive processes. Yet traditional brain imaging paradigms typically allow and record only minimal participant behavior, then reduce the recorded data to single map features of averaged responses. To more fully investigate the complex links between distributed brain dynamics and motivated natural behavior, we propose the development of wearable mobile brain/body imaging (MoBI) systems that continuously capture the wearers high-density electrical brain and muscle signals, three-dimensional body movements, audiovisual scene and point of regard, plus new data-driven analysis methods to model their interrelationships. The new imaging modality should allow new insights into how spatially distributed brain dynamics support natural human cognition and agency.

Visual evoked responses during standing and walking.

Human cognition has been shaped both by our body structure and by its complex interactions with its environment. Our cognition is thus inextricably linked to our own and others’ motor behavior. To model brain activity associated with natural cognition, we propose recording the concurrent brain dynamics and body movements of human subjects performing normal actions. Here we tested the feasibility of such a mobile brain/body (MoBI) imaging approach by recording high-density electroencephalographic (EEG) activity and body movements of subjects standing or walking on a treadmill while performing a visual oddball response task. Independent component analysis of the EEG data revealed visual event-related potentials that during standing, slow walking, and fast walking did not differ across movement conditions, demonstrating the viability of recording brain activity accompanying cognitive processes during whole body movement. Non-invasive and relatively low-cost MoBI studies of normal, motivated actions might improve understanding of interactions between brain and body dynamics leading to more complete biological models of cognition.

Electrophysiological markers of visual dimension changes and response changes

In cross-dimensional visual search tasks, target discrimination is faster when the previous trial contained a target defined in the same visual dimension as the current trial. The dimension-weighting account (DWA; A. Found & H. J. Müller, 1996) explains this intertrial facilitation by assuming that visual dimensions are weighted at an early perceptual stage of processing. Recently, this view has been challenged by models claiming that intertrial facilitation effects are generated at later stages that follow attentional target selection (K. Mortier, J. Theeuwes, & P. A. Starreveld, 2005). To determine whether intertrial facilitation is generated at a perceptual stage, at the response selection stage, or both, the authors focused on specific event-related brain potential components (directly linkable to perceptual and response-related processing) during a compound search task. Visual dimension repetitions were mirrored by shorter latencies and enhanced amplitudes of the N2-posterior- contralateral, suggesting a facilitated allocation of attentional resources to the target. Response repetitions and changes systematically modulated the lateralized readiness potential amplitude, suggesting a benefit from residual activations of the previous trial biasing the correct response. Overall, the present findings strengthen the DWA by indicating a perceptual origin of dimension change costs in visual search.

Beamforming in Noninvasive Brain–Computer Interfaces

Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subjects intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.

Biosensor Technologies for Augmented Brain–Computer Interfaces in the Next Decades

The study of brain-computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of real-life applications.

On the embodiment of emotion regulation: interoceptive awareness facilitates reappraisal

The ability to cognitively regulate emotional responses to aversive events is essential for mental and physical health. One prerequisite of successful emotion regulation is the awareness of emotional states, which in turn is associated with the awareness of bodily signals [interoceptive awareness (IA)]. This study investigated the neural dynamics of reappraisal of emotional responses in 28 participants who differed with respect to IA. Electroencephalography was used to characterize the time course of emotion regulation. We found that reappraisal was accompanied by reduced arousal and significant modulation of late neural responses. What is more, higher IA facilitated downregulation of affect and was associated with more pronounced modulation of underlying neural activity. Therefore, we conclude that IA not only advances the consolidation of somatic markers required for guiding individual behaviour but also creates processing advantages in tasks referring to these bodily markers.

Impaired central processing of emotional faces in anorexia nervosa.

Objectives: To elucidate the potential relationship between classification of emotional faces and impaired central processing in eating disorders and to investigate the potential mediatory role of alexithymia and depression in this relationship. Methods: Visual-evoked potentials (VEPs) to emotional faces and classification performance were assessed in 12 anorexic females and matched healthy controls. Results: Patients with anorexia nervosa showed no modulation of emotional face processing and displayed significantly increased N200 amplitudes in response to all emotional categories and decreased VEPs in response to unpleasant emotional faces in the P300 time range as compared with healthy controls. They also made more mistakes in emotional face recognition, in particular, for neutral, sad, and disgusted content. Conclusions: There are marked differences in evoked potentials and emotion recognition performances of patients with anorexia nervosa and controls in facial processing. Differences in brain dynamics might contribute to difficulties in the correct recognition of facially expressed emotions, deficits in social functioning, and in turn the maintenance of eating disorders. ED = eating disorder; ERP = event-related potential; VEP = visual-evoked potential; AN = anorexia nervosa; MDD = major depressive disorder; EEG = electroencephalography; TAS = Toronto Alexithymia Scale; BMI = body mass index; BDI = Beck Depression Inventory; STAI = State Trait Anxiety Inventory.