Validity of Time Reversal for Testing Granger Causality

Winkler, Irene,Panknin, Danny,Bartz, Daniel,Muller, Klaus-Robert,Haufe, Stefan Institute of Electrical and Electronics Engineers 2016 IEEE transactions on signal processing Vol.64 No.11

<P>Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-reversed data. They show that this procedure, time-reversed Granger causality (TRGC), robustly rejects causal interpretations on mixtures of independent signals. While promising results have been achieved in simulations, it was so far unknown whether time reversal leads to valid measures of information flow in the presence of true interaction. Here, we prove that, for linear finite-order autoregressive processes with unidirectional information flow between two variables, the application of time reversal for testing Granger causality indeed leads to correct estimates of information flow and its directionality. Using simulations, we further show that TRGC is able to infer correct directionality with similar statistical power as the net Granger causality between two variables, while being much more robust to the presence of measurement noise.</P>

Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

Vidaurre, C.,Ramos Murguialday, A.,Haufe, S.,Gó,mez, M.,Mü,ller, K.-R.,Nikulin, V.V. ACADEMIC PRESS 2019 NEUROIMAGE Vol.199 No.-

<P><B>Abstract</B></P> <P>An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).</P> <P><B>Highlights</B></P> <P> <UL> <LI> Afferent stimulation (STM) in the calibration phase was used to enhance BCI performance. </LI> <LI> Concurrent motor imagery and STM had stronger modulation of sensorimotor oscillations. </LI> <LI> STM significantly improved BCI accuracy particularly for poorly performing subjects. </LI> <LI> Classifiers trained with STM can be successfully used online even without stimulation. </LI> <LI> These findings ease the practical applicability of STM-based BCI systems. </LI> </UL> </P>

Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Dahne, Sven,Biessmann, Felix,Samek, Wojciech,Haufe, Stefan,Goltz, Dominique,Gundlach, Christopher,Villringer, Arno,Fazli, Siamac,Muller, Klaus-Robert IEEE 2015 Proceedings of the Institute of Electrical and Ele Vol.103 No.9

<P>Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.</P>

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

Habermehl, Christina,Steinbrink, Jens,Mü,ller, Klaus-Robert,Haufe, Stefan SPIE - International Society for Optical Engineeri 2014 JOURNAL OF BIOMEDICAL OPTICS Vol.19 No.9

<P>Functional near-infrared spectroscopy (fNIRS) is an optical method for noninvasively determining brain activation by estimating changes in the absorption of near-infrared light. Diffuse optical tomography (DOT) extends fNIRS by applying overlapping high density measurements, and thus providing a three-dimensional imaging with an improved spatial resolution. Reconstructing brain activation images with DOT requires solving an underdetermined inverse problem with far more unknowns in the volume than in the surface measurements. All methods of solving this type of inverse problem rely on regularization and the choice of corresponding regularization or convergence criteria. While several regularization methods are available, it is unclear how well suited they are for cerebral functional DOT in a semi-infinite geometry. Furthermore, the regularization parameter is often chosen without an independent evaluation, and it may be tempting to choose the solution that matches a hypothesis and rejects the other. In this simulation study, we start out by demonstrating how the quality of cerebral DOT reconstructions is altered with the choice of the regularization parameter for different methods. To independently select the regularization parameter, we propose a cross-validation procedure which achieves a reconstruction quality close to the optimum. Additionally, we compare the outcome of seven different image reconstruction methods for cerebral functional DOT. The methods selected include reconstruction procedures that are already widely used for cerebral DOT [minimum l2-norm estimate (l2MNE) and truncated singular value decomposition], recently proposed sparse reconstruction algorithms [minimum l1- and a smooth minimum l0-norm estimate (l1MNE, l0MNE, respectively)] and a depth- and noise-weighted minimum norm (wMNE). Furthermore, we expand the range of algorithms for DOT by adapting two EEG-source localization algorithms [sparse basis field expansions and linearly constrained minimum variance (LCMV) beamforming]. Independent of the applied noise level, we find that the LCMV beamformer is best for single spot activations with perfect location and focality of the results, whereas the minimum l1-norm estimate succeeds with multiple targets.</P>

Literary Negotiations in Contemporary Zainichi Korean Literature: Zainichi Korean Postcoloniality and its Entanglement with Global History

Maren Haufs-Brusberg 서울대학교 규장각한국학연구원 2023 Seoul journal of Korean studies Vol.36 No.2

Zainichi Korean literature, which addresses questions concerning the Zainichi Korean minority, can be considered as one among many postcolonial literatures. By examining works of Sagisawa Megumu, Kaneshiro Kazuki, and Kim Masumi as case studies, I position contemporary Zainichi Korean literature within the broader context of postcolonial global history. Sagisawa’s novel Saihate no futari (Two persons at the margins, 1999) narrates the relationship between a Japanese woman, whose father is an American GI, and a Zainichi Korean man. After the man succumbs to leukemia, the woman discovers that his mother was a survivor of the atomic bomb. The silencing of his mother’s voice can be analyzed using Spivak’s concept of the subaltern. Kaneshiro’s novel GO (2000) addresses Korea’s division as a consequence of imperialism and the Cold War. Furthermore, it draws connections between African Americans in the United States and the Zainichi Korean minority, which can be interpreted as an allusion to Bhabha’s concept of mimicry. In Kim Masumi’s novel Nason no sora (The sky of Nason, 2001), a Zainichi Korean woman residing in the United States engages with both the Japanese expatriate community and Asian Americans, contending with essentialist concepts of ethnicity. I argue that in the selected novels both the literary negotiations of Zainichi Korean postcoloniality and its entanglement with global history as well as the references to other diasporas, namely, the Asian and African diasporas in the United States, contribute to a subversive reframing of some prevailing narratives concerning the Zainichi Korean minority in Japan.