High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials

Ince, Nuri F.; Gupta, Rahul; Arıca, Sami; Tewfik, A.H.; Ashe, James; Pellizzer, Giuseppe

doi:10.1371/journal.pone.0014384

Cited by 72 publications

(48 citation statements)

References 43 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that critical information for decoding lower limb kinematics is contained in the smoothed AM in the lower half of the so-called delta band (i.e., 0.1-4 Hz) is consistent with recent EEG, electrocorticographic (ECoG), and local field potential (LFP) upper limb movement decoding studies that use the fluctuations in the amplitude of highly smoothed signals for decoding (Acharya et al 2010;Ball et al 2009;Ince et al 2010;Lv et al 2010;Waldert et al 2008;Zhuang et al 2010). It is also consistent with observations by Gwin et al (2010), who showed that meaningful changes during walking or running occur at low frequencies (Ͻ10 Hz) in high-density EEG.…”

Section: Discussionsupporting

confidence: 71%

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Presacco

Goodman²,

Forrester

et al. 2011

Journal of Neurophysiology

190

199

View full text Add to dashboard Cite

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. brain computer interface; brain-machine interface; electroencephalography LITTLE IS KNOWN about the organization, neural network mechanisms, and computations underlying the control of walking in humans (Choi and Bastian 2007). Although central pattern generators for locomotion are important in the control of walking, supraspinal networks, including the brain stem, cerebellum, and cortex, must be critical, as demonstrated by the changing motor and cognitive (i.e., spatial attention) demands imposed by bipedal walking in unknown or cluttered dynamic environments (Choi and Bastian 2007;Grillner et al. 2008;Nielsen 2003;Rossignol et al. 2007). Neuroimaging studies show that rhythmic foot or leg movements recruit primary motor cortex (Christensen et al. 2001;Dobkin et al. 2004;Heuninckx et al. 2005Heuninckx et al. , 2008Luft et al. 2002;Sahyoun et al. 2004), whereas electrophysiological investigations demonstrate electrocortical potentials related to lower limb movements (Wieser et al. 2010), as well as a greater involvement of human cortex during steady-speed locomotion than previously thought (Gwin et al. , 2011. In this regard, studies using functional near-infrared spectroscopy (fNIRS) show involvement of frontal, premotor, and supplementary motor areas during walking (Harada et al. 2009;Miyai et al. 2001;Suzuki et al. 2004Suzuki et al. , 2008. That primary sensorimotor cortices carry information about bipedal locomotion has been directly proven by the work of Nicolelis and colleagues (Fitzsimmons et al. 2009), who demonstrated that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to predict the kinematics of bipedal wa...

show abstract

Section: Discussionsupporting

confidence: 71%

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Presacco

Goodman²,

Forrester

et al. 2011

Journal of Neurophysiology

190

199

View full text Add to dashboard Cite

show abstract

“…For example, O'Leary and Hatsopoulos have shown very large interanimal differences in LFP-␤ measured in PMd (O'Leary and Hatsopoulos 2006). Second, LFP-␤ has often been found to be not as reliable as the low frequency bands for decoding performance during reach-andgrasp tasks (Bansal et al 2011;Ince et al 2010) because of its instability. Third, sometimes the LFP-␤ in premotor cortex might be completely absent, unlike in M1 (Spinks et al 2008).…”

Section: Discussionmentioning

confidence: 99%

Synchronization patterns suggest different functional organization in parietal reach region and dorsal premotor cortex

Chakrabarti

Martinez-Vazquez

Gail

2014

Journal of Neurophysiology

View full text Add to dashboard Cite

Chakrabarti S, Martinez-Vazquez P, Gail A. Synchronization patterns suggest different functional organization in parietal reach region and dorsal premotor cortex. J Neurophysiol 112: 3138-3153, 2014. First published September 17, 2014 doi:10.1152/jn.00621.2013.-The parietal reach region (PRR) and dorsal premotor cortex (PMd) form part of the fronto-parietal reach network. While neural selectivity profiles of single-cell activity in these areas can be remarkably similar, other data suggest that both areas serve different computational functions in visually guided reaching. Here we test the hypothesis that different neural functional organizations characterized by different neural synchronization patterns might be underlying the putatively different functional roles. We use cross-correlation analysis on single-unit activity (SUA) and multiunit activity (MUA) to determine the prevalence of synchronized neural ensembles within each area. First, we reliably find synchronization in PRR but not in PMd. Second, we demonstrate that synchronization in PRR is present in different cognitive states, including "idle" states prior to task-relevant instructions and without neural tuning. Third, we show that local field potentials (LFPs) in PRR but not PMd are characterized by an increased power and spike field coherence in the beta frequency range (12-30 Hz), further indicating stronger synchrony in PRR compared with PMd. Finally, we show that neurons with similar tuning properties tend to be correlated in their random spike rate fluctuations in PRR but not in PMd. Our data support the idea that PRR and PMd, despite striking similarity in single-cell tuning properties, are characterized by unequal local functional organization, which likely reflects different network architectures to support different functional roles within the fronto-parietal reach network.

show abstract

“…A number of recent studies have proposed the use of non-invasive methods, in particular the electroencephalography (EEG) signal, for decoding reaching directions (Mehring et al, 2003; Waldert et al, 2008; Ince et al, 2010) and continuous trajectories (Wolpaw and McFarland, 2004; Bradberry et al, 2010). Nevertheless most of these studies, in particular those focused on decoding movement direction (Connolly et al, 2003; Mehring et al, 2003; Musallam et al, 2004; Rickert et al, 2005; Rizzuto et al, 2005; Hammon et al, 2008; Waldert et al, 2008; Robinson et al, 2013), rely on cue-based protocols (i.e., where a “go” cue is used to instruct the subject to perform the movement at a fixed time).…”

Section: Introductionmentioning

confidence: 99%

Single trial prediction of self-paced reaching directions from EEG signals

Lew

Chavarriaga

Silvoni³

et al. 2014

Front. Neurosci.

View full text Add to dashboard Cite

Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.

show abstract

High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials

Cited by 72 publications

References 43 publications

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Synchronization patterns suggest different functional organization in parietal reach region and dorsal premotor cortex

Single trial prediction of self-paced reaching directions from EEG signals

Contact Info

Product

Resources

About