Eric Maris

Bio: Eric Maris is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Magnetoencephalography & Stimulus (physiology). The author has an hindex of 34, co-authored 101 publications receiving 16568 citations. Previous affiliations of Eric Maris include Nijmegen Institute for Cognition and Information & University of Helsinki.

FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

Abstract: This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

Theta and gamma oscillations predict encoding and retrieval of declarative memory.

Abstract: Although studies in animals and patients have demonstrated that brain oscillations play a role in declarative memory encoding and retrieval, little has been done to investigate the temporal dynamics and sources of brain activity in healthy human subjects performing such tasks. In a magnetoencephalography study using pictorial stimuli, we have now identified oscillatory activity in the gamma (60-90 Hz) and theta (4.5-8.5 Hz) band during declarative memory operations in healthy participants. Both theta and gamma activity was stronger for the later remembered compared with the later forgotten items (the "subsequent memory effect"). In the retrieval session, theta and gamma activity was stronger for recognized items compared with correctly rejected new items (the "old/new effect"). The gamma activity was also stronger for recognized compared with forgotten old items (the "recognition effect"). The effects in the theta band were observed over right parietotemporal areas, whereas the sources of the effects in the gamma band were identified in Brodmann area 18/19. We propose that the theta activity is directly engaged in mnemonic operations. The increase in neuronal synchronization in the gamma band in occipital areas may result in a stronger drive to subsequent areas, thus facilitating both memory encoding and retrieval. Alternatively, the gamma synchronization might reflect representations being reinforced by top-down activity from higher-level memory areas. Our results provide additional insight on human declarative memory operations and oscillatory brain activity that complements previous electrophysiological and brain imaging studies.

Two Sides of the Same Coin Speech and Gesture Mutually Interact to Enhance Comprehension

Abstract: Gesture and speech are assumed to form an integrated system during language production. Based on this view, we propose the integrated-systems hypothesis, which explains two ways in which gesture and speech are integrated--through mutual and obligatory interactions--in language comprehension. Experiment 1 presented participants with action primes (e.g., someone chopping vegetables) and bimodal speech and gesture targets. Participants related primes to targets more quickly and accurately when they contained congruent information (speech: "chop"; gesture: chop) than when they contained incongruent information (speech: "chop"; gesture: twist). Moreover, the strength of the incongruence affected processing, with fewer errors for weak incongruities (speech: "chop"; gesture: cut) than for strong incongruities (speech: "chop"; gesture: twist). Crucial for the integrated-systems hypothesis, this influence was bidirectional. Experiment 2 demonstrated that gesture's influence on speech was obligatory. The results confirm the integrated-systems hypothesis and demonstrate that gesture and speech form an integrated system in language comprehension.

Estimating Multiple Classification Latent Class Models.

Abstract: This paper presents a new class of models for persons-by-items data. The essential new feature of this class is the representation of the persons: every person is represented by its membership tomultiple latent classes, each of which belongs to onelatent classification. The models can be considered as a formalization of the hypothesis that the responses come about in a process that involves the application of a number ofmental operations. Two algorithms for maximum likelihood (ML) and maximum a posteriori (MAP) estimation are described. They both make use of the tractability of the complete data likelihood to maximize the observed data likelihood. Properties of the MAP estimators (i.e., uniqueness and goodness-of-recovery) and the existence of asymptotic standard errors were examined in a simulation study. Then, one of these models is applied to the responses to a set of fraction addition problems. Finally, the models are compared to some related models in the literature.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

Abstract: This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or