MEG Decoding Across Subjects

Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. The extreme difficulty is related to the structural and functional variability across the subjects. We call this approach "decoding across subjects". In this work, we address the problem of decoding across subjects for magnetoencephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. Third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one

Fast, invariant representation for human action in the visual system

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition, however, the underlying neural computations remain poorly understood. We use magnetoencephalography (MEG) decoding and a dataset of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discounts changes in 3D viewpoint relative to when it initially discriminates between actions. We measure the latency difference between invariant and non-invariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. Our results show no difference in decoding latency or temporal profile between invariant and non-invariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time, and that both form and motion information are crucial for fast, invariant action recognition

MEG:hen perustuvan aivo-tietokone -käyttöliittymän kehitys

Brain–computer interfaces (BCI) have recently gained interest both in basic neuroscience and clinical interventions. The majority of noninvasive BCIs measure brain activity with electroencephalography (EEG). However, the real-time signal analysis and decoding of brain activity suffer from low signal-to-noise ratio and poor spatial resolution of EEG. These limitations could be overcome by using magnetoencephalography (MEG) as an alternative measurement modality. The aim of this thesis is to develop an MEG-based BCI for decoding hand motor imagery, which could eventually serve as a therapeutic method for patients recovering from e.g. cerebral stroke. Here, machine learning methods for decoding motor imagery -related brain activity are validated with healthy subjects’ MEG measurements. The first part of the thesis (Study I) involves a comparison of feature extraction methods for classifying left- vs right-hand motor imagery (MI), and MI vs rest. It was found that spatial filtering and further extraction of bandpower features yield better classification accuracy than time–frequency features extracted from parietal gradiometers. Furthermore, prior spatial filtering improved the discrimination capability of time–frequency features. The training data for a BCI is typically collected in the beginning of each measurement session. However, as this can be time-consuming and exhausting for the subject, the training data from other subjects’ measurements could be used as well. In the second part of the thesis (Study II), methods for across-subject classification of MI were compared. The results showed that a classifier based on multi-task learning with a l2,1-norm regularized logistic regression was the best method for across-subject decoding for both MEG and EEG. In Study II, we also compared the decoding results of simultaneously measured EEG and MEG data, and investigated whether the MEG responses to passive hand movements could be used to train a classifier to detect MI. MEG yielded altogether slightly, but not significantly, better results than EEG. Training the classifiers with subject’s own or other subjects’ passive movements did not result in high accuracy, which indicates that passive movements should not be used for calibrating an MI-BCI. The methods presented in this thesis are suitable for a real-time MEG-based BCI. The decoding results can be used as a benchmark when developing other classifiers specifically for motor imagery -related MEG data.Aivo-tietokone -käyttöliittymät (brain–computer interface; BCI) ovat viime aikoina herättäneet kiinnostusta niin neurotieteen perustutkimuksessa kuin kliinisissä interventioissakin. Suurin osa ei-invasiivisista BCI:stä mittaa aivotoimintaa elektroenkefalografialla (EEG). EEG:n matala signaali-kohinasuhde ja huono avaruudellinen resoluutio kuitenkin hankaloittavat reaaliaikais-ta signaalianalyysia ja aivotoiminnan luokittelua. Nämä rajoitteet voidaan kiertää käyttämällä magnetoenkefalografiaa (MEG) vaihtoehtoisena mittausmenetelmänä. Tämän työn tavoitteena on kehittää käden liikkeen kuvittelua luokitteleva, MEG:hen perustuva BCI, jota voidaan myöhemmin käyttää terapeuttisena menetelmänä esimerkiksi aivoinfarktista toipuvien potilaiden kuntoutuk-sessa. Tutkimuksessa validoidaan terveillä koehenkilöillä tehtyjen MEG-mittausten perusteella koneoppimismenetelmiä, joilla luokitellaan liikkeen kuvittelun aiheuttamaa aivotoimintaa. Ensimmäisessä osatyössä (Tutkimus I) vertailtiin piirteenirrotusmenetelmiä, joita käytetään erottamaan toisistaan vasemman ja oikean käden kuvittelu sekä liikkeen kuvittelu ja lepotila. Ha-vaittiin, että avaruudellisesti suodatettujen signaalien taajuuskaistan teho luokittelupiirteenä tuotti parempia luokittelutarkkuuksia kuin parietaalisista gradiometreistä mitatut aika-taajuuspiirteet. Lisäksi edeltävä avaruudellinen suodatus paransi aika-taajuuspiirteiden erottelukykyä luokittelu-tehtävissä.BCI:n opetusdata kerätään yleensä kunkin mittauskerran alussa. Koska tämä voi kuitenkin olla aikaavievää ja uuvuttavaa koehenkilölle, opetusdatana voidaan käyttää myös muilta koehenkilöiltä kerättyjä mittaussignaaleja. Toisessa osatyössä (Tutkimus II) vertailtiin koehenkilöiden väliseen luo-kitteluun soveltuvia menetelmiä. Tulosten perusteella monitehtäväoppimista ja l2,1-regularisoitua logistista regressiota käyttävä luokittelija oli paras menetelmä koehenkilöiden väliseen luokitteluun sekä MEG:llä että EEG:llä. Toisessa osatyössä vertailtiin myös samanaikaisesti mitattujen MEG:n ja EEG:n tuottamia luokit-telutuloksia, sekä tutkittiin voidaanko passiivisten kädenliikkeiden aikaansaamia MEG-vasteita käyttää liikkeen kuvittelua tunnistavien luokittelijoiden opetukseen. MEG tuotti hieman, muttei merkittävästi, parempia tuloksia kuin EEG. Luokittelijoiden opetus koehenkilöiden omilla tai mui-den koehenkilöiden passiiviliikkeillä ei tuottanut hyviä luokittelutarkkuuksia, mikä osoittaa että passiiviliikkeitä ei tulisi käyttää liikkeen kuvittelua tunnistavan BCI:n kalibrointiin. Työssä esitettyjä menetelmiä voidaan käyttää reaaliaikaisessa MEG-BCI:ssä. Luokittelutuloksia voidaan käyttää vertailukohtana kehitettäessä muita liikkeen kuvitteluun liittyvän MEG-datan luokittelijoita

An introduction to time-resolved decoding analysis for M/EEG

The human brain is constantly processing and integrating information in order to make decisions and interact with the world, for tasks from recognizing a familiar face to playing a game of tennis. These complex cognitive processes require communication between large populations of neurons. The non-invasive neuroimaging methods of electroencephalography (EEG) and magnetoencephalography (MEG) provide population measures of neural activity with millisecond precision that allow us to study the temporal dynamics of cognitive processes. However, multi-sensor M/EEG data is inherently high dimensional, making it difficult to parse important signal from noise. Multivariate pattern analysis (MVPA) or "decoding" methods offer vast potential for understanding high-dimensional M/EEG neural data. MVPA can be used to distinguish between different conditions and map the time courses of various neural processes, from basic sensory processing to high-level cognitive processes. In this chapter, we discuss the practical aspects of performing decoding analyses on M/EEG data as well as the limitations of the method, and then we discuss some applications for understanding representational dynamics in the human brain

Sparse Predictive Structure of Deconvolved Functional Brain Networks

The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods. Furthermore most network estimation methods cannot distinguish between real and spurious correlation arising from the convolution due to nodes' interaction, which thus introduces additional noise in the data. We propose a machine learning pipeline aimed at identifying multivariate differences between brain networks associated to different experimental conditions. The pipeline (1) leverages the deconvolved individual contribution of each edge and (2) maps the task into a sparse classification problem in order to construct the associated "sparse deconvolved predictive network", i.e., a graph with the same nodes of those compared but whose edge weights are defined by their relevance for out of sample predictions in classification. We present an application of the proposed method by decoding the covert attention direction (left or right) based on the single-trial functional connectivity matrix extracted from high-frequency magnetoencephalography (MEG) data. Our results demonstrate how network deconvolution matched with sparse classification methods outperforms typical approaches for MEG decoding

Analytical methods and experimental approaches for electrophysiological studies of brain oscillations

Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour