arXiv subjects

Mohammad Moghadamfalahi

Publications and source records attributed to Mohammad Moghadamfalahi.

5 recordsLinked to original sources

arXiv2017

EEG-assisted Modulation of Sound Sources in the Auditory Scene

Noninvasive EEG (electroencephalography) based auditory attention detection could be useful for improved hearing aids in the future. This work is a novel attempt to investigate the feasibility of online modulation of sound sources by probabilistic detection of auditory attention, using a noninvasive EEG-based brain computer interface. Proposed online system modulates the upcoming sound sources through gain adaptation which employs probabilistic decisions (soft decisions) from a classifier trained on offline calibration data. In this work, calibration EEG data were collected in sessions where the participants listened to two sound sources (one attended and one unattended). Cross-correlation coefficients between the EEG measurements and the attended and unattended sound source envelope (estimates) are used to show differences in sharpness and delays of neural responses for attended versus unattended sound source. Salient features to distinguish attended sources from the unattended ones in the correlation patterns have been identified, and later they have been used to train an auditory attention classifier. Compared to the existing results in the literature, in this paper we have two main contributions. First, using the auditory attention classifier, we have shown high offline detection performance with single channel EEG measurements of shorter duration compared to the existing approaches in the literature which employ large number of channels with longer EEG measurements. Second, using the classifier trained offline in the calibration session, we have shown the performance of the online sound source modulation system. We observe that online sound source modulation system is able to keep the level of attended sound source higher than the unattended source. Keywords: auditory BCI, cocktail party problem, auditory attention classification

q-bio.NC↗

arXiv2017

Decoding Complex Imagery Hand Gestures

Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study, we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy--intended grasp prediction probability--of 64.5\% for 8 different hand gestures, more than 5 times the chance level.

cs.HC↗

arXiv2017

Context-Aware Recursive Bayesian Graph Traversal in BCIs

Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism. In this approach, user moves a pointer to the desired vertex in the graph in which each vertex represents an action. To select a vertex, a Select command, or a proposed probabilistic Selection criterion (PSC) can be used to automatically detect the user intended vertex. Performance of different PGMs and Selection criteria combinations are compared over a keyboard based on a graph layout. Based on the simulation results, probabilistic Selection criterion along with the probabilistic graphical model provides the highest performance boost for individuals with pour calibration performance and achieving the same performance for individuals with high calibration performance.

cs.HC↗

arXiv2016

An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed.

cs.HC↗

arXiv2016

Fast Switch Scanning Keyboards: Minimal Expected Query Decision Trees

Augmentative and Alternative Communication (AAC) systems allow people with disabilities to provide input to devices which empower them to more fully interact with their environment. Within AAC, switch scanning is a common paradigm for spelling where a set of characters is highlighted and the user is queried as to whether their target character is in the highlighted set. These queries are used to traverse a decision tree which successively prunes away characters until only a single one remains (the estimate). This work seeks a decision tree which requires the fewest expected queries per decision sequence (EQPD). In particular, we remove the constraint that the decision tree needs to be a row-item or group-row-item style tree and minimize EQPD. We pose the problem as a Huffman code with variable, integer cost and solve it with a mild extension of Golin's method in "A dynamic programming algorithm for constructing optimal prefix-free codes with unequal letter costs", IEEE Transactions on Information Theory (1998). Additionally, we model the user on the query level by their probability of detection and false alarm to derive their expected performance on the character level given some decision tree. We perform experiments which show that the min EQPD decision tree (Karp) may reduce selection times, especially for timed (single switch) switch scanning.

cs.HC↗