Alessandro Presacco

University of Houston, Houston, Texas, United States

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Publications (7)12.18 Total impact

  • Source
    Alessandro Presacco · Larry W Forrester · Jose L Contreras-Vidal ·
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    ABSTRACT: Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra- and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was ~0.68±0.08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis.
    IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 03/2012; 20(2):212-9. DOI:10.1109/TNSRE.2012.2188304 · 3.19 Impact Factor
  • Source
    Jose L. Contreras-Vidal · Alessandro Presacco · Harshavardhan Agashe · Andrew Paek ·
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    ABSTRACT: This article highlights recent advances in the design of noninvasive neural interfaces based on the scalp electroencephalogram (EEG). The simplest of physical tasks, such as turning the page to read this article, requires an intense burst of brain activity. It happens in milliseconds and requires little conscious thought. But for amputees and stroke victims with diminished motor-sensory skills, this process can be difficult or impossible. Our team at the University of Maryland, in conjunction with the Johns Hopkins Applied Physics Laboratory (APL) and the University of Maryland School of Medicine, hopes to offer these people newfound mobility and dexterity. In separate research thrusts, were using data gleaned from scalp EEG to develop reliable brainmachine interface (BMI) systems that could soon control modern devices such as prosthetic limbs or powered robotic exoskeletons.
    IEEE Pulse 01/2012; 3(1):34-7. DOI:10.1109/MPUL.2011.2175635 · 0.96 Impact Factor
  • Alessandro Presacco · Larry Forrester · Jose L Contreras-Vidal ·
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    ABSTRACT: Before 2009, the feasibility of applying brain-machine interfaces (BMIs) to control prosthetic devices had been limited to upper limb prosthetics such as the DARPA modular prosthetic limb. Until recently, it was believed that the control of bipedal locomotion involved central pattern generators with little supraspinal control. Analysis of cortical dynamics with electroencephalography (EEG) was also prevented by the lack of analysis tools to deal with excessive signal artifacts associated with walking. Recently, Nicolelis and colleagues paved the way for the decoding of locomotion showing that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to decode bipedal kinematics in rhesus monkeys. However, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This study uses non-invasive EEG signals to decode human walking in six nondisabled adults. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs, to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular kinematics of the left and right hip, knee and ankle joints and EEG were recorded concurrently. Our results support the possibility of decoding human bipedal locomotion with EEG. The average of the correlation values (r) between predicted and recorded kinematics for the six subjects was 0.7 (± 0.12) for the right leg and 0.66 (± 0.11) for the left leg. The average signal-to-noise ratio (SNR) values for the predicted parameters were 3.36 (± 1.89) dB for the right leg and 2.79 (± 1.33) dB for the left leg. These results show the feasibility of developing non-invasive neural interfaces for volitional control of devices aimed at restoring human gait function.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:4588-91. DOI:10.1109/IEMBS.2011.6091136
  • Source
    Alessandro Presacco · Ronald Goodman · Larry Forrester · Jose Luis Contreras-Vidal ·
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    ABSTRACT: Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function.
    Journal of Neurophysiology 07/2011; 106(4):1875-87. DOI:10.1152/jn.00104.2011 · 2.89 Impact Factor
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    ABSTRACT: Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebellar ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. However, open-loop reinforcement of motor control programs may hold promise as a technique to improve motor performance. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG-based BCI provide advanced biofeedback to improve motor imagery and provide a “backdoor” to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.
    Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 04/2011
  • Source
    Shrivats Iyer · Anil Maybhate · Alessandro Presacco · Angelo H All ·
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    ABSTRACT: The motor evoked potential (MEP) is an electrical response of peripheral neuro-muscular pathways to stimulation of the motor cortex. MEPs provide objective assessment of electrical conduction through the associated neural pathways, and therefore detect disruption due to a nervous system injury such as spinal cord injury (SCI). In our studies of SCI, we developed a novel, multi-channel set-up for MEP acquisition in rat models. Unlike existing electrophysiological systems for SCI assessment, the set-up allows for multi-channel MEP acquisition from all limbs of rats and enables longitudinal monitoring of injury and treatment for in vivo models of experimental SCI. The article describes the development of the set-up and discusses its capabilities to acquire MEPs in rat models of SCI. We demonstrate its use for MEP acquisition under two types of anesthesia as well as a range of cortical stimulation parameters, identifying parameters yielding consistent and reliable MEPs. To validate our set-up, MEPs were recorded from a group of 10 rats before and after contusive SCI. Upon contusion with moderate severity (12.5mm impact height), MEP amplitude decreased by 91.36±6.03%. A corresponding decline of 93.8±11.4% was seen in the motor behavioral score (BBB), a gold standard in rodent models of SCI.
    Journal of Neuroscience Methods 11/2010; 193(2):210-6. DOI:10.1016/j.jneumeth.2010.08.017 · 2.05 Impact Factor
  • Alessandro Presacco · Jorge Bohórquez · Erdem Yavuz · Ozcan Ozdamar ·
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    ABSTRACT: The nature of the auditory steady-state responses (ASSR) evoked with 40-Hz click trains and their relationship to auditory brainstem and middle latency responses (ABR/MLR), gamma band responses (GBR) and beta band responses (BBR) were investigated using superposition theory. Transient responses obtained by continuous loop averaging deconvolution (CLAD) and last click responses (LCR) were used to synthesize ASSRs and GBRs. ASSRs were obtained with trains of low jittered 40 Hz clicks presented monaurally and deconvolved using a modified CLAD. Resulting transient responses and modified LCRs were used to predict the ASSRs and the GBR. The ABR/MLR obtained with deconvolution predicted accurately the steady portion of the ASSR but failed to predict its onset portion. The modified LCR failed to fully predict both portions. The GBRs were predicted by narrow band filtering of the ASSRs. Significant BBR activity was found both in the ASSRs and deconvolved ABR/MLRs. Simulations using deconvolved ABR/MLRs obtained at 40 Hz predict fully the steady state but not the onset portion of the ASSRs, thus confirming the superposition theory. Click rate adaptation plays a significant role in ASSR generation with click trains and should be considered in evaluating convolved response generation theories.
    Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 04/2010; 121(9):1540-50. DOI:10.1016/j.clinph.2010.03.020 · 3.10 Impact Factor

Publication Stats

109 Citations
12.18 Total Impact Points


  • 2012
    • University of Houston
      • Department of Electrical & Computer Engineering
      Houston, Texas, United States
  • 2011-2012
    • University of Maryland, College Park
      • Department of Kinesiology
      Maryland, United States
    • Loyola University Maryland
      Baltimore, Maryland, United States
  • 2010
    • University of Miami
      • Department of Biomedical Engineering
      كورال غيبلز، فلوريدا, Florida, United States
    • Johns Hopkins University
      • Department of Biomedical Engineering
      Baltimore, Maryland, United States