RESUMEN
Neural recordings frequently get contaminated by ECG or pulsation artifacts. These large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. The current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. The denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised data.Clinical Relevance- Pulsation artifact can mask relevant neural activity patterns and make their visual inspection difficult. Using sparse signal representation, we established a new approach to reconstruct the quasiperiodic pulsation template and computed the residue signal to achieve noise-free neural activity.
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Artefactos , Compresión de Datos , Electroencefalografía/métodos , Procesamiento de Señales Asistido por Computador , AlgoritmosRESUMEN
Recording from the human brain at the spatiotemporal resolution of action potentials provides critical insight into mechanisms of higher cognitive functions and neuropsychiatric disease that is challenging to derive from animal models. Here, organic materials and conformable electronics are employed to create an integrated neural interface device compatible with minimally invasive neurosurgical procedures and geared toward chronic implantation on the surface of the human brain. Data generated with these devices enable identification and characterization of individual, spatially distribute human cortical neurons in the absence of any tissue penetration (n = 229 single units). Putative single-units are effectively clustered, and found to possess features characteristic of pyramidal cells and interneurons, as well as identifiable microcircuit interactions. Human neurons exhibit consistent phase modulation by oscillatory activity and a variety of population coupling responses. The parameters are furthermore established to optimize the yield and quality of single-unit activity from the cortical surface, enhancing the ability to investigate human neural network mechanisms without breaching the tissue interface and increasing the information that can be safely derived from neurophysiological monitoring.
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Neuronas , Células Piramidales , Potenciales de Acción/fisiología , Animales , Encéfalo , Humanos , Interneuronas , Neuronas/fisiologíaRESUMEN
Objective.Somatosensory evoked potentials (SSEPs) recorded with electrocorticography (ECoG) for central sulcus (CS) identification is a widely accepted procedure in routine intraoperative neurophysiological monitoring. Clinical practices test the short-latency SSEPs for the phase reversal over strip electrodes. However, assessments based on waveform morphology are susceptible to variations in interpretations due to the hand area's localized nature and usually require multiple electrode placements or electrode relocation. We investigated the feasibility of unsupervised delineation of the CS by using the spatiotemporal patterns of the SSEP captured with the ECoG grid.Approach. Intraoperatively, SSEPs were recorded from eight patients using ECoG grids placed over the sensorimotor cortex. Neurosurgeons blinded to the electrophysiology identified the sensory and motor gyri using neuronavigation based on sulcal anatomy. We quantified the most discriminatory time points in SSEPs temporal profile between the primary motor (M1) and somatosensory (S1) cortex using the Fisher discrimination criterion. We visualized the amplitude gradient of the SSEP over a 2D heat map to provide visual feedback for the delineation of the CS based on electrophysiology. Subsequently, we employed spectral clustering using the entire the SSEP waveform without selecting any time points and grouped ECoG channels in an unsupervised fashion.Main results.Consistently in all patients, two different time points provided almost equal discrimination between anterior and posterior channels, which vividly outlined the CS when we viewed the SSEP amplitude distribution as a spatial 2D heat map. The first discriminative time point was in proximity to the conventionally favored â¼20 ms peak (N20), and the second time point was slightly later than the markedly high â¼30 ms peak (P30). Still, the location of these time points varied noticeably across subjects. Unsupervised clustering approach separated the anterior and posterior channels with an accuracy of 96.3% based on the time derivative of the SSEP trace without the need for a subject-specific time point selection. In contrast, the raw trace resulted in an accuracy of 88.0%.Significance. We show that the unsupervised clustering of the SSEP trace assessed with subdural electrode grids can delineate the CS automatically with high precision, and the constructed heat maps can localize the motor cortex. We anticipate that the spatiotemporal patterns of SSEP fused with machine learning can serve as a useful tool to assist in surgical planning.
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Monitorización Neurofisiológica Intraoperatoria , Corteza Motora , Potenciales Evocados Somatosensoriales , Mano , Humanos , Aprendizaje Automático no SupervisadoRESUMEN
Sleep is a fundamental homeostatic process, and disorders of sleep can greatly affect quality of life. Parkinson's disease (PD) is highly comorbid for a spectrum of sleep disorders and deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been reported to improve sleep architecture in PD. We studied local field potential (LFP) recordings in PD subjects undergoing STN-DBS over the course of a full-night's sleep. We examined the changes in oscillatory activity recorded from STN between ultradian sleep states to determine whether sleep-stage dependent spectral patterns might reflect underlying dysfunction. For this study, PD (n=10) subjects were assessed with concurrent polysomnography and LFP recordings from the DBS electrodes, for an average of 7.5 hours in 'off' dopaminergic medication state. Across subjects, we found conserved spectral patterns among the canonical frequency bands (delta 0-3 Hz, theta 3-7 Hz, alpha 7-13 Hz, beta 13-30 Hz, gamma 30-90 Hz and high frequency 90-350 Hz) that were associated with specific sleep cycles: delta (0-3 Hz) activity during non-rapid eye movement (NREM) associated stages was greater than during Awake, whereas beta (13-30 Hz) activity during NREM states was lower than Awake and rapid eye movement (REM). In addition, all frequency bands were significantly different between NREM states and REM. However, each individual subject exhibited a unique mosaic of spectral interrelationships between frequency bands. Our work suggests that LFP recordings from human STN differentiate between sleep cycle states, and sleep-state specific spectral mosaics may provide insight into mechanisms underlying sleep pathophysiology.
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Estimulación Encefálica Profunda , Enfermedad de Parkinson/complicaciones , Fases del Sueño , Núcleo Subtalámico/fisiopatología , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , PolisomnografíaRESUMEN
OBJECTIVE: High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are considered as promising clinical biomarkers of epileptogenic regions in the brain. The aim of this study is to improve and automatize the detection of HFOs by exploring the time-frequency content of iEEG and to investigate the seizure onset zone (SOZ) detection accuracy during the sleep, awake and pre-ictal states in patients with epilepsy, for the purpose of assisting the localization of SOZ in clinical practice. APPROACH: Ten-minute iEEG segments were defined during different states in eight patients with refractory epilepsy. A three-stage algorithm was implemented to detect HFOs in these segments. First, an amplitude based initial detection threshold was used to generate a large pool of HFO candidates. Then distinguishing features were extracted from the time and time-frequency domain of the raw iEEG and used with a Gaussian mixture model clustering to isolate HFO events from other activities. The spatial distribution of HFO clusters was correlated with the seizure onset channels identified by neurologists in seven patient with good surgical outcome. MAIN RESULTS: The overlapping rates of localized channels and seizure onset locations were high in all states. The best result was obtained using the iEEG data during sleep, achieving a sensitivity of 81%, and a specificity of 96%. The channels with maximum number of HFOs identified epileptogenic areas where the seizures occurred more frequently. SIGNIFICANCE: The current study was conducted using iEEG data collected in realistic clinical conditions without channel pre-exclusion. HFOs were investigated with novel features extracted from the entire frequency band, and were correlated with SOZ in different states. The results indicate that automatic HFO detection with unsupervised clustering methods exploring the time-frequency content of raw iEEG can be efficiently used to identify the epileptogenic zone with an accurate and efficient manner.
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Potenciales de Acción/fisiología , Ondas Encefálicas/fisiología , Convulsiones/diagnóstico , Convulsiones/fisiopatología , Adulto , Automatización/métodos , Electroencefalografía/métodos , Epilepsia/diagnóstico , Epilepsia/fisiopatología , Femenino , Humanos , Masculino , Persona de Mediana Edad , Factores de TiempoRESUMEN
PURPOSE: In this study, we investigate the modification to cortical oscillations of patients with Parkinson disease (PD) by subthalamic deep brain stimulation (STN-DBS). METHODS: Spontaneous cortical oscillations of patients with PD were recorded with magnetoencephalography during on and off subthalamic nucleus deep brain stimulation states. Several features such as average frequency, average power, and relative subband power in regions of interest were extracted in the frequency domain, and these features were correlated with Unified Parkinson Disease Rating Scale III evaluation. The same features were also investigated in patients with PD without surgery and healthy controls. RESULTS: Patients with Parkinson disease without surgery compared with healthy controls had a significantly lower average frequency and an increased average power in 1 to 48 Hz range in whole cortex. Higher relative power in theta and simultaneous decrease in beta and gamma over temporal and occipital were also observed in patients with PD. The Unified Parkinson Disease Rating Scale III rigidity score correlated with the average frequency and with the relative power of beta and gamma in frontal areas. During subthalamic nucleus deep brain stimulation, the average frequency increased significantly when stimulation was on compared with off state. In addition, the relative power dropped in delta, whereas it rose in beta over the whole cortex. Through the course of stimulation, the Unified Parkinson Disease Rating Scale III rigidity and tremor scores correlated with the relative power of alpha over left parietal. CONCLUSIONS: Subthalamic nucleus deep brain stimulation improves the symptoms of PD by suppressing the synchronization of alpha rhythm in somatomotor region.
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Encéfalo/fisiopatología , Estimulación Encefálica Profunda , Enfermedad de Parkinson/fisiopatología , Enfermedad de Parkinson/terapia , Adulto , Anciano , Femenino , Humanos , Magnetoencefalografía , Masculino , Persona de Mediana Edad , Descanso , Núcleo SubtalámicoRESUMEN
BACKGROUND: Deep brain stimulation (DBS) for the treatment of movement disorders has provided researchers with an opportunity to record electrical oscillatory activity from electrodes implanted in deep brain structures. Extracellular activity recorded from a population of neurons, termed local field potentials (LFPs), has shed light on the pathophysiology of movement disorders and holds the potential to lead to refinement in existing treatments. OBJECTIVE: This paper reviews the clinical significance of LFPs recorded from macroelectrodes implanted in basal ganglia and thalamic targets for the treatment of Parkinson's disease, essential tremor and dystonia. METHODS: Neural population dynamics and subthreshold events, which are undetectable by single-unit recordings, can be examined with frequency band analysis of LFPs (frequency range: 1-250 Hz). RESULTS: Of clinical relevance, reliable correlations between motor symptoms and components of the LFP power spectrum suggest that LFPs may serve as a biomarker for movement disorders. In particular, Parkinson's rigidity has been shown to correlate with the power of beta oscillations (13-30 Hz), and essential tremor coheres with oscillations of 8-27 Hz. Furthermore, evidence indicates that the optimal contacts for DBS programming can be predicted from the anatomic location of beta and gamma bands (48-200 Hz). CONCLUSION: LFP analysis has implications for improved electrode targeting and the development of a real-time, individualized, 'closed-loop' stimulation system.
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Ondas Encefálicas , Estimulación Encefálica Profunda , Modelos Neurológicos , Trastornos del Movimiento/terapia , Neuronas/fisiología , Potenciales de Acción , Discinesias/fisiopatología , Discinesias/terapia , Distonía/fisiopatología , Distonía/terapia , Electrodos Implantados , Diseño de Equipo , Temblor Esencial/fisiopatología , Temblor Esencial/terapia , Humanos , Potenciales de la Membrana , Microelectrodos , Neuronas/clasificación , Enfermedad de Parkinson/fisiopatología , Enfermedad de Parkinson/terapia , Evaluación de SíntomasRESUMEN
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus is an effective treatment for Parkinson disease. However, DBS is not responsive to an individual's disease state, and programming parameters, once established, do not change to reflect disease state. Local field potentials (LFPs) recorded from DBS electrodes are being investigated as potential biomarkers for the Parkinson disease state. However, no patient data exist about what happens to LFPs over the lifetime of the implant. OBJECTIVE: We investigated whether LFP amplitude and response to limb movement differed between patients implanted acutely with subthalamic nucleus DBS electrodes and patients implanted 2 to 7 years previously. METHODS: We recorded LFPs at DBS surgery time (9 subjects), 3 weeks after initial placement (9 subjects), and 2 to 7 years (median: 3.5) later during implanted programmable generator replacement (11 sides). LFP power-frequency spectra for each of 3 bipolar electrode derivations of adjacent contacts were calculated over 5-minute resting and 30-second movement epochs. Monopolar impedance data were used to evaluate trends over time. RESULTS: There was no significant difference in ß-band LFP amplitude between initial electrode implantation (OR) and 3-week post-OR times (P=.94). However, ß-band amplitude was lower at implanted programmable generator replacement times than in OR (P=.008) and post-OR recordings (P=.039). Impedance measurements declined over time (P<.001). CONCLUSION: Postoperative LFP activity can be recorded years after DBS implantation and demonstrates a similar profile in response to movement as during acute recordings, although amplitude may decrease. These results support the feasibility of constructing a closed-loop, patient-responsive DBS device based on LFP activity.
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Ritmo beta/fisiología , Estimulación Encefálica Profunda/métodos , Enfermedad de Parkinson/fisiopatología , Adulto , Anciano , Electrodos Implantados , Electroencefalografía , Femenino , Mano/inervación , Mano/fisiopatología , Humanos , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Movimiento/fisiología , Enfermedad de Parkinson/terapia , Análisis Espectral , Núcleo Subtalámico/fisiología , Factores de TiempoRESUMEN
BACKGROUND: 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. OBJECTIVE: We investigated the relationship between local field potential (LFP) activities in the subthalamic nucleus and the therapeutic response to programming. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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Estimulación Encefálica Profunda/métodos , Enfermedad de Parkinson/fisiopatología , Enfermedad de Parkinson/terapia , Núcleo Subtalámico/fisiopatología , Anciano , Ritmo beta , Estimulación Encefálica Profunda/efectos adversos , Electrodos Implantados , Electroencefalografía , Fenómenos Electrofisiológicos , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Movimiento/fisiología , Procedimientos Neuroquirúrgicos , Descanso/fisiología , TemblorRESUMEN
Multichannel neural activities such as EEG or ECoG in a brain computer interface can be classified with subset selection algorithms running on large feature dictionaries describing subject specific features in spectral, temporal and spatial domain. While providing high accuracies in classification, the subset selection techniques are associated with long training times due to the large feature set constructed from multichannel neural recordings. In this paper we study a novel denoising technique for reducing the dimensionality of the feature space which decreases the computational complexity of the subset selection step radically without causing any degradation in the final classification accuracy. The denoising procedure was based on the comparison of the energy in a particular time segment and in a given scale/level to the energy of the raw data. By setting denoising threshold a priori the algorithm removes those nodes which fail to capture the energy in the raw data in a given scale. We provide experimental studies towards the classification of motor imagery related multichannel ECoG recordings for a brain computer interface. The denoising procedure was able to reach the same classification accuracy without denoising and a computational complexity around 5 times smaller. We also note that in some cases the denoised procedure performed better classification.
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Algoritmos , Inteligencia Artificial , Encéfalo/fisiología , Electroencefalografía/métodos , Potenciales Evocados/fisiología , Almacenamiento y Recuperación de la Información/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Interfaz Usuario-Computador , Bases de Datos Factuales , Humanos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
We present a new method for the classification of EEG in a brain computer interface by adapting subject specific features in spectral, temporal and spatial domain. For this particular purpose we extend our previous work on ECoG classification based on structural feature dictionary and apply it to extract the spectro-temporal patterns of multichannel EEG recordings related to a motor imagery task. The construction of the feature dictionary based on undecimated wavelet packet transform is extended to block FFT. We evaluate several subset selection algorithms to select a small number of features for final classification. We tested our proposed approach on five subjects of BCI Competition 2005 dataset- IVa. By adapting the wavelet filter for each subject, the algorithm achieved an average classification accuracy of 91.4% The classification results and characteristic of selected features indicate that the proposed algorithm can jointly adapt to EEG patterns in spectro-spatio-temporal domain and provide classification accuracies as good as existing methods used in the literature.
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Algoritmos , Inteligencia Artificial , Electroencefalografía/métodos , Potenciales Evocados Motores/fisiología , Imaginación/fisiología , Corteza Motora/fisiología , Reconocimiento de Normas Patrones Automatizadas/métodos , Interfaz Usuario-Computador , Electroencefalografía/clasificación , Humanos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C(3)/C(4) electrode locations of the standard 10/20 system is adaptively segmented in time, over a non-dyadic grid by maximizing the probabilistic distances between expansion coefficients corresponding to left and right hand movement imagery. This is followed by a frequency domain clustering procedure in each adapted time segment to maximize the discrimination power of the resulting time-frequency features. Then, the most discriminant features from the resulting arbitrarily segmented time-frequency plane are sorted. A principal component analysis (PCA) step is applied to reduce the dimensionality of the feature space. This reduced feature set is finally fed to a linear discriminant for classification. The online step simply computes the reduced dimensionality features determined by the offline step and feeds them to the linear discriminant. We provide experimental data to show that the method can adapt to physio-anatomical differences, subject-specific and hemisphere-specific motor imagery patterns. The algorithm was applied to all nine subjects of the BCI Competition 2002. The classification performance of the proposed algorithm varied between 70% and 92.6% across subjects using just two electrodes. The average classification accuracy was 80.6%. For comparison, we also implemented an adaptive autoregressive model based classification procedure that achieved an average error rate of 76.3% on the same subjects, and higher error rates than the proposed approach on each individual subject.