James Ashe

University of Minnesota Duluth, Duluth, Minnesota, United States

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Publications (73)388.57 Total impact

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  • James Ashe · Khalaf Bushara
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    ABSTRACT: The cerebellum, and the olivo-cerebellar system in particular, may be the central mechanism of a neural clock that provides a rhythmic neural signal used to time motor and cognitive processes. Several independent lines of evidence support this hypothesis. First, the resting membrane potential of neurons in the inferior olive oscillates at ~10 Hz and the neural input from the olive leads to rhythmic complex spikes in cerebellum Purkinje cells. Second, the repeating modular microstructure of the cerebellum is ideally suited for performing computations underlying a basic neural process such as timing. Third, damage to the cerebellum leads to deficits in the perception of time and in the production of timed movements. Fourth, functional imaging studies in human subjects have shown activation of the inferior olive specifically during time perception. However, additional data on the exact role of rhythmic cerebellar activity during basis motor and sensory processing will be necessary before the hypothesis that the cerebellum is a neural clock is more widely accepted.
    Advances in Experimental Medicine and Biology 10/2014; 829:155-65. DOI:10.1007/978-1-4939-1782-2_9 · 1.96 Impact Factor
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    ABSTRACT: The proceedings of the workshop synthesize the experimental, preclinical, and clinical data suggesting that the cerebellum, basal ganglia (BG), and their connections play an important role in pathophysiology of various movement disorders (like Parkinson's disease and atypical parkinsonian syndromes) or neurodevelopmental disorders (like autism). The contributions from individual distinguished speakers cover the neuroanatomical research of complex networks, neuroimaging data showing that the cerebellum and BG are connected to a wide range of other central nervous system structures involved in movement control. Especially, the cerebellum plays a more complex role in how the brain functions than previously thought.
    The Cerebellum 09/2014; DOI:10.1007/s12311-014-0595-y · 2.72 Impact Factor
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    ABSTRACT: Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on the other day. This paper provides a solution in the form of adaptive spatial features. We propose an algorithm to capture the local spatial variability of LFP patterns and provide accurate long-term decoding. This algorithm achieved more than 95% decoding of eight movement directions two weeks after its initial training.
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    ABSTRACT: One of the challenges in using intra-cortical recordings like Local Field Potentials for Brain Computer Interface (BCI) is their inherent day-to-day variability and non-stationarity caused by subject motivation and learning. Practical Brain Computer Interfaces need to overcome these variations, as models trained on characteristic features from one day fail to represent new characteristics of another. This paper proposes a novel adaptive model that adjusts to signal variation by appending new features to the existing model and without knowledge of actual hand kinetics in an unsupervised way. With this adapting model we investigated the effects of learning and model adaptation on BCI performance. Using this new model we dramatically improve on all previously published long term decoding and show that target direction is accurately decoded in 95% of the trials over two weeks and in 85% of the trials in varying environments. Since the model needs no separate re-calibration, it can reduce user frustration and improve BCI experience.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: Local Field Potential (LFP) recordings are one type of intracortical recordings, (besides Single Unit Activity) that can help decode movement direction successfully. In the longterm however, using LFPs for decoding presents some major challenges like inherent instability and non-stationarity. Our approach to overcome this challenge bases around the hypothesis that each task has a signature source-location pattern. The methodology involves introduction of source localization, and tracking of sources over a period of time that enables us to decode movement direction in an eight-direction center-out-reach-task. We establish that such tracking can be used for long term decoding, with preliminary results indicating consistent patterns. In fact, tracking task related source locations render up to 66% accuracy in decoding movement direction one week after the decoding model was learnt.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:838-841. DOI:10.1109/EMBC.2013.6609631
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    ABSTRACT: A major drawback of using Local Field Potentials (LFP) for Brain Computer Interface (BCI) is their inherent instability and non-stationarity. Specifically, even when a well-trained subject performs the same task over a period of time, the neural data observed are unstable. To overcome this problem in decoding movement direction, this paper proposes the use of qualitative information in the form of spatial patterns of inter-channel ranking of multi-channel LFP recordings. The quality of the decoding was further refined by concentrating on the statistical distributions of the top powered channels. Decoding of movement direction was performed using Support Vector Machines (SVM) to construct decoders, instead of the traditional spatial patterns. Our algorithm provides a decoding power of up to 74% on average over a period of two weeks, compared with the state-of-the-art methods in the literature that yield only 33%. Furthermore, it provides 62.5% direction decoding in novel motor environments, compared with 29.5% with conventional methods. Finally, a comparison with the traditional methods and other surveyed literature is presented.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:4623-6. DOI:10.1109/EMBC.2012.6346997
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    Xiang Wu · James Ashe · Khalaf O Bushara
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    ABSTRACT: The timing of events can be implicit or without awareness yet critical for task performance. However, the neural correlates of implicit timing are unknown. One system that has long been implicated in event timing is the olivocerebellar system, which originates exclusively from the inferior olive. By using event-related functional MRI in human subjects and a specially designed behavioral task, we examined the effect of the subjects' awareness of changes in stimulus timing on the olivocerebellar system response. Subjects were scanned while observing changes in stimulus timing that were presented near each subject's detection threshold such that subjects were aware of such changes in only approximately half the trials. The inferior olive and multiple areas within the cerebellar cortex showed a robust response to time changes regardless of whether the subjects were aware of these changes. Our findings provide support to the proposed role of the olivocerebellar system in encoding temporal information and further suggest that this system can operate independently of awareness and mediate implicit timing in a multitude of perceptual and motor operations, including classical conditioning and implicit learning.
    Proceedings of the National Academy of Sciences 08/2011; 108(33):13818-22. DOI:10.1073/pnas.1104096108 · 9.67 Impact Factor
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    ABSTRACT: This paper introduces a novel technique to address the instability and time variability challenges associated with brain activity recorded on different days. A critical challenge when working with brain signal activity is the variability in their characteristics when the signals are collected in different sessions separated by a day or more. Such variability is due to the acute and chronic responses of the brain tissue after implantation, variations as the subject learns to optimize performance, physiological changes in a subject due to prior activity or rest periods and environmental conditions. We propose a novel approach to tackle signal variability by focusing on learning subspaces which are recurrent over time. Furthermore, we illustrate how we can use projections on those subspaces to improve classification for an application such as brain-machine interface (BMI). In this paper, we illustrate the merits of finding recurrent subspaces in the context of movement direction decoding using local field potential (LFP). We introduce two methods for using the learned subspaces in movement direction decoding and show a decoding power improvement from 76% to 88% for a particularly unstable subject and consistent decoding across subjects.
    IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 06/2011; 19(3):240-8. DOI:10.1109/TNSRE.2011.2106802 · 3.19 Impact Factor
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    ABSTRACT: A main disadvantage of using intra-cortical recordings for Brain Computer Interface (BCI) is their inherent non-stationarity and instability. Thus developing direction de- coders for Local Field Potentials (LFP) that are robust over time becomes a difficult task. In this paper, we show the superior performance of qualitative information over the absolute power of the recorded signals by introducing a novel method,that uses time-evolving spatial patterns. This method over-performs the baseline method by 30% on an average over a two week testing period and provides a bit-rate of 0.98 per trial. Further, these spatial-patterns provide robustness against learning when new field-forces are introduced.
    International IEEE/EMBS Conference on Neural Engineering 01/2011; DOI:10.1109/NER.2011.5910569
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    ABSTRACT: In Brain Machine Interface (BMI), movement direction can be decoded using intra-cortical recordings such as Local Field Potentials (LFP). Due to the natural instability and non-stationarity of these recordings, it is difficult to develop decoders that remain consistent over time and are not affected by learning. This paper uses qualitative information based on the temporal and spatial distribution of inter-channel ranking. The image block processing technique is exploited, and the distribution of top ranked channels is calculated. We use this spatio-temporal distribution information to decode the movement direction via a maximum likelihood estimator. Our results indicate that the decoding power is consistent over a period of two weeks. On an average, we obtain an average classification accuracy of 51.9% versus 33.2% from traditional state-of-the-art technique over a two week period.
    Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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    ABSTRACT: The current development of brain-machine interface technology is limited, among other factors, by concerns about the long-term stability of single- and multi-unit neural signals. In addition, the understanding of the relation between potentially more stable neural signals, such as local field potentials, and motor behavior is still in its early stages. We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction. In particular, we examined local field potentials (LFP), which are thought to be more stable over time than single unit activity (SUA). Using LFP recordings from chronically implanted electrodes in the dorsal premotor and primary motor cortex of non-human primates trained to make arm movements in different directions, we made the following observations: (i) it is possible to decode movement target direction with high fidelity from the spatial correlation patterns of neural activity in both primary motor (M1) and dorsal premotor cortex (PMd); (ii) the decoding accuracy of LFP was similar to the decoding accuracy obtained with the set of SUA recorded simultaneously; (iii) directional information varied with the LFP frequency sub-band, being greater in low (0.3-4 Hz) and high (48-200 Hz) frequency bands than in intermediate bands; (iv) the amount of directional information was similar in M1 and PMd; (v) reliable decoding was achieved well in advance of movement onset; and (vi) LFP were relatively stable over a period of one week. The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction. This finding suggests that parameters of movement, such as target direction, have a stable spatial distribution within primary motor and dorsal premotor cortex, which may be used for brain-machine interfaces.
    PLoS ONE 12/2010; 5(12):e14384. DOI:10.1371/journal.pone.0014384 · 3.23 Impact Factor
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    X Wu · I Nestrasil · J Ashe · P Tuite · K Bushara
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    ABSTRACT: The unique anatomical and electrophysiological features of the inferior olive and its importance to cerebellar function have been recognized for decades. However, understanding the exact function of the inferior olive has been limited by the general lack of correlation between its neural activity and specific behavioral states. Electrophysiological studies in animals showed that the inferior olive response to sensory stimuli is generally invariant to stimulus properties but is enhanced by unexpected stimuli. Using functional magnetic resonance imaging in humans, we have shown that the inferior olive is activated when subjects performed a task requiring perception of visual stimuli with unpredictable timing (Xu et al. J Neurosci 26(22):5990-5995, 2006, Liu et al. J Neurophysiol 100(3):1557-1561, 2008). In the current study, subjects were scanned while passively perceiving visual and tactile stimuli that were rendered unpredictable by continuously varying interstimulus intervals (ISIs). Sequences of visual stimuli and tactile stimuli to the right hand were presented separately within the same scanning session. In addition to the activation of multiple areas in the cerebellar cortex consistent with previous imaging studies, the results show that both tactile and visual stimulation with variable ISIs were effective in activating the inferior olive. Together with our previous findings, the current results are consistent with the electrophysiological studies in animals and further support the view that the inferior olive and the climbing fiber system primarily convey the temporal information of sensory input regardless of the modality.
    The Cerebellum 12/2010; 9(4):598-602. DOI:10.1007/s12311-010-0203-8 · 2.72 Impact Factor
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    ABSTRACT: The neural mechanisms involved in motor timing are subcortical, involving mainly cerebellum and basal ganglia. However, the role played by these structures in predictive motor timing is not well understood. Unlike motor timing, which is often tested using rhythm production tasks, predictive motor timing requires visuo-motor coordination in anticipation of a future event, and it is evident in behaviors such as catching a ball or shooting a moving target. We examined the role of the cerebellum and striatum in predictive motor timing in a target interception task in healthy (n = 12) individuals and in subjects (n = 9) with spinocerebellar ataxia types 6 and 8. The performance of the healthy subjects was better than that of the spinocerebellar ataxia. Successful performance in both groups was associated with increased activity in the cerebellum (right dentate nucleus, left uvula (lobule V), and lobule VI), thalamus, and in several cortical areas. The superior performance in the controls was related to activation in thalamus, putamen (lentiform nucleus) and cerebellum (right dentate nucleus and culmen-lobule IV), which were not activated either in the spinocerebellar subjects or within a subgroup of controls who performed poorly. Both the cerebellum and the basal ganglia are necessary for the predictive motor timing. The degeneration of the cerebellum associated with spinocerebellar types 6 and 8 appears to lead to quantitative rather than qualitative deficits in temporal processing. The lack of any areas with greater activity in the spinocerebellar group than in controls suggests that limited functional reorganization occurs in this condition.
    The Cerebellum 11/2010; 10(2):233-44. DOI:10.1007/s12311-010-0237-y · 2.72 Impact Factor
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    ABSTRACT: In the United States, the most commonly used surgical treatment for patients with Parkinson's disease is the implantation of deep brain stimulation (DBS) electrodes within the subthalamic nucleus. However, DBS device programming remains difficult and is a possible source of decreased efficacy. We investigated the relationship between local field potential (LFP) activities in the subthalamic nucleus and the therapeutic response to programming. We recorded LFPs with macroelectrodes placed unilaterally for DBS in 4 PD patients, 3 weeks after implantation, before the start of log-term DBS. Power-frequency spectra were calculated for each of 7 possible electrode contacts or contact pairs, over multiple 5- to 10-minute quiet waking epochs and over 30-second epochs during hand movements. Subsequently, DBS devices were programmed, with testing to determine which electrode contacts or contact pairs demonstrated optimal therapeutic efficacy. For each patient, the contact pair found to provide optimal efficacy was associated with the highest energy in the beta (13-32 Hz) and gamma (48-220 Hz) bands during postoperative LFP recordings at rest and during hand movements. Activities in other frequency bands did not show significant correlations between LFP power and optimal electrode contacts. Postoperative subband analysis of LFP recordings in beta and gamma frequency ranges may be used to select optimal electrode contacts. These results indicate that LFP recordings from implanted DBS electrodes can provide important clues to guide the optimization of DBS therapy in individual patients.
    Neurosurgery 08/2010; 67(2):390-7. DOI:10.1227/01.NEU.0000372091.64824.63 · 3.62 Impact Factor
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    ABSTRACT: Movement direction for Brain Machine Interface (BMI) can be decoded successfully using Local Field Potentials (LFP) and Single Unit Activity (SUA). A major challenge when dealing with the intra-cortical recordings is to develop decoders that are robust in time. In this paper we present for the first time a technique that uses the qualitative information derived from multiple LFP channels rather than the absolute power of the recorded signals. In this novel method, we use a power based inter-channel ranking system to define the quality of a channel in multi-channel LFP. This representation enables us to bypass the problems associated with the dynamic ranges of absolute power. We also introduce a parameter based ranking system that provides the same rank to channels that have comparable powers. We show that using our algorithms, we can develop models that provide stable decoding of eight movement directions with an average efficiency of above 56% over a period of two weeks. Moreover, the decoding power using this method is 46% at the end of two weeks versus the 13% using the traditional approaches. We also applied these models to decoding movements performed in a force field and again achieved significantly higher decoding power than the existing methods.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:4825-8. DOI:10.1109/IEMBS.2010.5627909
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    ABSTRACT: Local Field Potentials (LFP) provides higher spatial resolution and SNR than EEG data and can be used to construct a Brain Computer Interface. In, we have shown that movement direction decoding can be done with about 90 % classification accuracy using spatial patterns (CSP) and Error Correction Output Codes (ECOC). However, a major challenge in this study is to make this method more robust to inter-session variability of the LFP data, where state-of-the-art results are in the high 70 percent. In, we have demonstrated that LFP features that are recurrent across sessions can be extracted using a subspace learning method and used to improve the CSP +ECOC classifier. In this work, we propose an extension of the subspace learning method that exploits the spatial topology of the channels. This allows us to learn spatially diverse features, while previously the subspaces were being learned independently of the channel layout. We proposed a method where a block of samples from neighboring channels is used to find the subspaces and decode the directions. This approach is analogous to analyzing an 8×8 pixel map in image processing. Furthermore, this method allows a spatio-temporal classification, and it is indeed observed that different directions were providing higher accuracies at different time blocks. The proposed method can boosts the accuracy by at least 6% to bring classification to the mid 80 percent. Furthermore, we show early results where adding a pilot trial from the test session can be used as a calibration to further improve the spatio-temporal classification.
    Signals, Systems and Computers, 2009 Conference Record of the Forty-Third Asilomar Conference on; 12/2009
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    ABSTRACT: Similar to the occipital alpha rhythm, electroencephalographic (EEG) signals in the alpha- and beta-frequency bands can be suppressed by movement or motor imagery and have thus been thought to represent the "idling state" of the sensorimotor cortex. A negative correlation between spontaneous alpha EEG and blood-oxygen-level-dependent (BOLD) signals has been reported in combined EEG and fMRI (functional Magnetic Resonance Imaging) experiments when subjects stayed at the resting state or alternated between the resting state and a task. However, the precise nature of the task-induced alpha modulation remains elusive. It was not clear whether alpha/beta rhythm suppressions may co-vary with BOLD when conducting tasks involving varying activations of the cortex. Here, we quantified the task-evoked responses of BOLD and alpha/beta-band power of EEG directly in the cortical source domain, by using source imaging technology, and examined their covariation across task conditions in a mixed block and event-related design. In this study, 13 subjects performed tasks of right-hand, right-foot or left-hand movement and motor imagery when EEG and fMRI data were separately collected. Task-induced increase of BOLD signal and decrease of EEG amplitudes in alpha and beta bands were shown to be co-localized at the somatotopic sensorimotor cortex. At the corresponding regions, the reciprocal changes of the two signals co-varied in the magnitudes across imagination and movement conditions. The spatial correspondence and negative covariation between the two measurements were further shown to exist at somatotopic brain regions associated with different body parts. These results suggest an inverse functional coupling relationship between task-induced changes of BOLD and low-frequency EEG signals.
    NeuroImage 10/2009; 49(3):2596-606. DOI:10.1016/j.neuroimage.2009.10.028 · 6.36 Impact Factor
  • Rahul Gupta · James Ashe
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    ABSTRACT: Brain-machine interfaces (BMIs) hold a lot of promise for restoring some level of motor function to patients with neuronal disease or injury. Current BMI approaches fall into two broad categories--those that decode discrete properties of limb movement (such as movement direction and movement intent) and those that decode continuous variables (such as position and velocity). However, to enable the prosthetic devices to be useful for common everyday tasks, precise control of the forces applied by the end-point of the prosthesis (e.g., the hand) is also essential. Here, we used linear regression and Kalman filter methods to show that neural activity recorded from the motor cortex of the monkey during movements in a force field can be used to decode the end-point forces applied by the subject successfully and with high fidelity. Furthermore, the models exhibit some generalization to novel task conditions. We also demonstrate how the simultaneous prediction of kinematics and kinetics can be easily achieved using the same framework, without any degradation in decoding quality. Our results represent a useful extension of the current BMI technology, making dynamic control of a prosthetic device a distinct possibility in the near future.
    IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 07/2009; 17(3):254-62. DOI:10.1109/TNSRE.2009.2023290 · 3.19 Impact Factor
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    ABSTRACT: We show that movement direction can be decoded with high accuracy using the spatial patterns extracted from multichannel local field potentials (LFPs). Two monkeys were trained to execute center-out movement in 8 directions. During the task the LFP activity was recorded with two 64 channel grids from the pre- and primary motor areas. The LFP signals were decomposed into 4 sub-band components in the 0-4 Hz, 4-10 Hz, 14-30 Hz and 48-200 Hz frequency ranges. The sub-band activity was post processed with regularized common spatial patterns algorithm and fed to linear discriminant analysis for final classification. Directions of movement were estimated using a redundant hierarchical classification strategy that tested groups of directions against diametrically opposite groups. The grouping of directions was based on the spatial correlation that we observed between LFP signals corresponding to neighboring movement directions which is similar to the cosine tuning profile of single neurons. We found that the decoding power for 8 directions was 80% and 92% for the two subjects, respectively, in 0-4 Hz frequency band. Our best result of 92% nearly doubles the accuracy of the best results reported in the literature with similar set-ups. These results indicate that spatial patterns in LFP can be used to construct high accuracy brain computer interfaces.
    Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on; 06/2009

Publication Stats

3k Citations
388.57 Total Impact Points


  • 2003–2014
    • University of Minnesota Duluth
      • Department of Electrical Engineering
      Duluth, Minnesota, United States
  • 1992–2011
    • Minneapolis Veterans Affairs Hospital
      Minneapolis, Minnesota, United States
  • 2010
    • Emory University
      Atlanta, Georgia, United States
  • 2009
    • University of Minnesota Twin Cities
      Minneapolis, Minnesota, United States
  • 1997–2009
    • Spokane VA Medical Center
      Spokane, Washington, United States
  • 2007
    • Virginia Polytechnic Institute and State University
      • Department of Psychology
      Blacksburg, Virginia, United States
  • 2000–2003
    • San Francisco VA Medical Center
      San Francisco, California, United States
  • 1998
    • Massachusetts Institute of Technology
      Cambridge, Massachusetts, United States