Measuring utility without mixing apples and oranges and eliciting beliefs about stock prices

In day-to-day life we encounter decisions amongst prospects that do not have a convex structure. To address this concern, Herstein and Milnor introduce mixture sets and provide necessary and sufficient conditions for a cardinal and linear utility representation. We derive the same utility representation for partial mixture sets: where the mixture operation is only partially defined. The resulting model has an interesting application to finance. In particular, we use paths instead of events to elicit utility and beliefs about stock prices. This feature is promising for settings where the dimension of the state space is large

A linear feature space for simultaneous learning of spatio-spectral filters in BCI

Item does not contain fulltextIt is shown how two of the most common types of feature mapping used for classification of single trial Electroencephalography (EEG), i.e. spatial and frequency filtering, can be equivalently performed as linear operations in the space of frequency-specific detector covariance tensors. Thus by first mapping the data to this space, a simple linear classifier can directly learn optimal spatial + frequency filters. Significantly, if the classifier’s loss function is convex, learning these filters is a convex minimisation problem. It is also shown how to pre-process the data such that the resulting decision function is robust to the biases inherent in EEG data. Further, based upon ideas from Max Margin Matrix Factorisation, it is shown how the trace norm can be used to select solutions which have low rank. Low rank solutions are preferred as they reflect prior information about the types of EEG signals we expect to see, i.e. that the classifiable information is contained in only a few spatio/spectral pairs. They are also easier to interpret. This feature-space transformation is compared with the Common-Spatial-Patterns on simulated and real Imagined Movement Brain Computer Interface (BCI) data and shown to give state-of-the-art performance.8 p

Detecting traces of consciousness in the process of intending to act

Contains fulltext : 157823.pdf (publisher's version ) (Open Access)An intention to act has different onsets when it is measured in different ways. When participants provide a self-initiated report on the onset of their awareness of intending to act, the report occurs around 150 ms prior to action. However, when the same participants are repeatedly asked about their awareness of intending at different points in time, the onset of intending is found up to 2 s prior to action. This 'probed' awareness has its onset around the same time as the brain starts preparing the act, as measured using EEG. First of all, this undermines straightforward interpretations about the temporal relation between unconscious brain states and conscious intentions and actions. Secondly, we suggest that these results present a problem for the view that intentions are mental states occurring at a single point in time. Instead, we suggest the results to support the interpretation of an intention to act as a multistage process developing over time. This process of intending seems to develop during the process of acting, leaving reportable traces in consciousness at certain points along the road.12 p

Free Wally: Where motor intentions meet reason and consequence

Contains fulltext : 207720.pdf (publisher's version ) (Open Access)To investigate the neural preparation and awareness of an intention to act, neuroscientists typically examine spontaneous movements: self-paced flexions of the hand or foot. However, these movements may not present a straightforward case of intended action as they are performed in absence of reasons to act and without the evaluation of action consequences. Therefore, a common criticism of these studies is that they lack ecological validity, because the results do not generalize to the more societally relevant deliberate actions that we perform in daily life. We agree that research on intended action should include reason-based deliberate actions in order to be more relevant for debates about human agency and free will. Therefore, we have developed a computer game called "Free Wally", which invites players to perform deliberate actions to achieve a goal. Free Wally provides a controlled environment for studying deliberate intended action, by presenting information for deciding whether or not to act, what action to perform and when to perform it, incorporating all basic components of an ecologically valid intended act. As a first step to validate our setup, we compare this game to a second computer game that measures spontaneous actions in a traditional way. While playing either game, the timing of the experienced intentions to act is measured using a real-time probing method. Moreover, the neural preparation for action is measured in terms of the (lateralized) readiness potential and alpha/beta event-related desynchronization across the motor cortex. No differences were found between the games in these last stages of action preparation, suggesting that the Free Wally game can be used to study intended action. However, differences in earlier stages during intention formation are to be expected. With Free Wally as a tool, we hope to encourage further research into the formation and content of ecologically valid motor intentions.17 p

Interactions between pre-processing and classification methods for event-related-potential classification

Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g. visual or tactile), ERP component (e.g. P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a “best-practice” method for ERP detection problems

Decoding speech perception by native and non-native speakers using single-trial electrophysiological data

Contains fulltext : 116445.pdf (publisher's version ) (Open Access)Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.13 p

P300 audio-visual speller

Item does not contain fulltextThe Farwell and Donchin matrix speller is well known as one of the highest performing brain-computer interfaces (BCIs) currently available. However, its use of visual stimulation limits its applicability to users with normal eyesight. Alternative BCI spelling systems which rely on non-visual stimulation, e. g. auditory or tactile, tend to perform much more poorly and/or can be very difficult to use. In this paper we present a novel extension of the matrix speller, based on flipping the letter matrix, which allows us to use the same interface for visual, auditory or simultaneous visual and auditory stimuli. In this way we aim to allow users to utilize the best available input modality for their situation, that is use visual + auditory for best performance and move smoothly to purely auditory when necessary, e. g. when disease causes the user's eyesight to deteriorate. Our results on seven healthy subjects demonstrate the effectiveness of this approach, with our modified visual + auditory stimulation slightly out-performing the classic matrix speller. The purely auditory system performance was lower than for visual stimulation, but comparable to other auditory BCI systems.6 p

Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing

Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we reported a novel paradigm for a BBVEP-based BCI that utilised a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a 6 x 6 visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only

Probing for intentions: Why clocks do not provide the only measurement of time

Having an intention to act is commonly operationalized as the moment at which awareness of an urge or decision to act arises. Measuring this moment has been challenging due to the dependence on first-person reports of subjective experience rather than objective behavioral or neural measurements. Commonly, this challenge is met using (variants of) Libet's clock method. In 2008, Matsuhashi and Hallett published a novel probing strategy as an alternative to the clock method. We believe their probe method could provide a valuable addition to the clock method because: it measures the timing of an intention in real-time, it can be combined with additional (tactile, visual or auditory) stimuli to create a more ecologically valid experimental context, and it allows the measurement of the point of no return. Yet to this date, the probe method has not been applied widely - possibly due to concerns about the effects that the probes might have on the intention and/or action preparation processes. To address these concerns, a 2x2 within-subject design is tested. In this design, two variables are manipulated: (1) the requirement of an introspection report and (2) the presence of an auditory probe. Three observables are measured that provide information about the timing of an intention to act: (1) awareness reports of the subjective experience of having an intention, (2) neural preparatory activity for action, and (3) behavioral data of the performed actions. The presence of probes was found to speed up mean action times by roughly 300 ms, but did not alter the neural preparation for action. The requirement of an introspection report did influence brain signals: reducing the amplitude of the readiness potential and increasing the desynchronization in the alpha and beta bands over the motor cortex prior to action onset. By discussing the strengths and weaknesses of the probe method compared to the clock method, we hope to demonstrate its added value and promote its use in future research