311 research outputs found
FingerFlex: Inferring Finger Trajectories from ECoG signals
Motor brain-computer interface (BCI) development relies critically on neural
time series decoding algorithms. Recent advances in deep learning architectures
allow for automatic feature selection to approximate higher-order dependencies
in data. This article presents the FingerFlex model - a convolutional
encoder-decoder architecture adapted for finger movement regression on
electrocorticographic (ECoG) brain data. State-of-the-art performance was
achieved on a publicly available BCI competition IV dataset 4 with a
correlation coefficient between true and predicted trajectories up to 0.74. The
presented method provides the opportunity for developing fully-functional
high-precision cortical motor brain-computer interfaces.Comment: 6 pages, 3 figures, 4 tables. Preprint. Under revie
Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charitรฉ โ Universitรคtsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Cientรญficas y Tรฉcnicas. Centro Cientรญfico Tecnolรณgico Conicet - Santa Fe. Instituto de Matemรกtica Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemรกtica Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Kรถhler, Richard. Charitรฉ โ Universitรคtsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charitรฉ โ Universitรคtsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charitรฉ โ Universitรคtsmedizin Berlin; Alemani
Cortical Orchestra Conducted by Purpose and Function
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์์ฐ๊ณผํ๋ํ ํ๋๊ณผ์ ๋๊ณผํ์ ๊ณต,2020. 2. ์ ์ฒ๊ธฐ.์ด๊ฐ๊ณผ ์๊ธฐ์์ฉ๊ฐ๊ฐ์ ์ฐ๋ฆฌ์ ์์กด ๋ฐ ์ผ์์ํ์ ์ ๋์ ์ธ ์ํฅ์ ๋ฏธ์น๋ ์ค์ํ ๊ฐ๊ฐ ๊ธฐ๋ฅ์ด๋ค. ๋ง์ด์ ๊ฒฝ๊ณ์์ ์ด ๋ ๊ฐ์ง ๊ธฐ๋ฅ๋ค์ ํ์ํ ์ ๋ณด๋ฅผ ์์งํ๊ณ ์ ๋ฌํ๋ ๊ธฐ๊ณ์ ์์ฉ๊ธฐ ๋ฐ ๊ทธ ๊ตฌ์ฌ์ฑ ์ ๊ฒฝ๋ค์ ๋ํ ์ ํธ ์ ๋ฌ ๋ฉ์ปค๋์ฆ ๋ฐ ๊ทธ ํน์ง๋ค์ ์๋์ ์ผ๋ก ์ ์๋ ค์ ธ ์๋ ํธ์ด๋ค. ๊ทธ๋ฌ๋, ์ด๊ฐ๊ณผ ์๊ธฐ์์ฉ๊ฐ๊ฐ์ ํ์ฑํ๊ธฐ ์ํ ์ธ๊ฐ ๋์ ํผ์ง์์์ ์ ๋ณด ์ฒ๋ฆฌ ๋ฉ์ปค๋์ฆ์ ๋ํ์ฌ ์ฐ๋ฆฌ๊ฐ ํ์ฌ ์๊ณ ์๋ ๋ฐ๋ ๊ทนํ ์ผ๋ถ๋ถ์ด๋ค. ์ด ๋
ผ๋ฌธ์์ ์ ์ํ๋ ์ผ๋ จ์ ์ฐ๊ตฌ๋ค์ ์ธ๊ฐ ๋ ํผ์ง ๋จ๊ณ์์ ์ด๊ฐ๊ณผ ์๊ธฐ์์ฉ๊ฐ๊ฐ์ ์ง๊ฐ์ ์ฒ๋ฆฌ๊ณผ์ ์ ๋ํ ๊ฑฐ์์ ์ ๊ฒฝ๊ณ ์ ๋ณด์ฒ๋ฆฌ ๋ฉ์ปค๋์ฆ์ ๋ค๋ฃฌ๋ค.
์ฒซ ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ๋ํผ์ง๋ํ๋ฅผ ์ด์ฉํ์ฌ ์ธ๊ฐ ์ผ์ฐจ ๋ฐ ์ด์ฐจ ์ฒด์ฑ๊ฐ๊ฐ ํผ์ง์์ ์ธ๊ณต์ ์ธ ์๊ทน๊ณผ ์ผ์์ํ์์ ์ ํ ์ ์๋ ์๊ทน์ ํฌํจํ๋ ๋ค์ํ ์ง๋์ด๊ฐ๊ฐ ๋ฐ ์ง๊ฐ ์๊ทน์ ๋ํ ๊ฑฐ์์ ์ ๊ฒฝ๊ณ ์ ๋ณด์ฒ๋ฆฌ ํน์ฑ์ ๋ฐํ๋ค. ์ด ์ฐ๊ตฌ์์๋ ์ผ์ฐจ ๋ฐ ์ด์ฐจ ์ฒด์ฑ๊ฐ๊ฐ ํผ์ง์ ์ด๊ฐ๊ฐ ์ฃผํ์ ํน์ด์ ์ธ ํ์ด-๊ฐ๋ง ์์ญ ์ ๊ฒฝํ๋์ด ์๊ทน ์ฃผํ์์ ๋ฐ๋ผ ๊ฐ๊ฐ ์์ดํ ์๊ฐ์ ๋ค์ด๋๋ฏน์ค๋ฅผ ๊ฐ์ง๊ณ ๋ณํํ๋ ๊ฒ์ ํ์ธํ์๋ค. ๋ํ, ์ด๋ฌํ ํ์ด-๊ฐ๋ง ํ๋์ ์ฑ๊ธด ์ง๊ฐ๊ณผ ๋ฏธ์ธํ ์
์๊ฐ์ ๊ฐ์ง ์์ฐ์ค๋ฌ์ด ์ง๊ฐ ์๊ทน์ ๋ํด์๋ ์ง๋์ด๊ฐ๊ฐ์ ๊ฒฝ์ฐ์ ์ ์ฌํ ํจํด์ ๋ณด์๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ค์ ์ธ๊ฐ์ ์ง๋์ด๊ฐ๊ฐ์ด ๋งค์ฐ ๋จ์ํ ํํ์ ์๊ทน์ผ์ง๋ผ๋ ๋๋ ์ฒด์ฑ๊ฐ๊ฐ ์์คํ
์ ์์ด ๊ฑฐ์์ ์ธ ๋ค์ค ์์ญ์์์ ๊ณ์ธต์ ์ ๋ณด์ฒ๋ฆฌ๋ฅผ ๋๋ฐํ๋ค๋ ์ ์ ์์ฌํ๋ค.
๋ ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ์ธ๊ฐ์ ์์ง์๊ณผ ๊ด๋ จ๋ ๋์ ์ฝ ์์ญ์์์ ํ์ด-๊ฐ๋ง ๋ํ์ฑ์ด ์๊ธฐ์์ฉ๊ฐ๊ฐ๊ณผ ๊ฐ์ ๋ง์ด์ ๊ฒฝ๊ณ๋ก๋ถํฐ์ ์ฒด์ฑ๊ฐ๊ฐ ํผ๋๋ฐฑ์ ์ฃผ๋ก ๋ฐ์ํ๋์ง, ์๋๋ฉด ์์ง์ ์ค๋น ๋ฐ ์ ์ด๋ฅผ ์ํ ํผ์ง ๊ฐ ์ ๊ฒฝ ํ๋ก์ธ์ค์ ๋ํ ํ๋์ ๋ฐ์ํ๋์ง๋ฅผ ์กฐ์ฌํ์๋ค. ์ฐ๊ตฌ ๊ฒฐ๊ณผ, ์๋ฐ์ ์ด๋ ์ค ๋๋ ์ด๋๊ฐ๊ฐ๋ น์์์ ํ์ด-๊ฐ๋ง ํ๋์ ์ผ์ฐจ ์ฒด์ฑ๊ฐ๊ฐํผ์ง์ด ์ผ์ฐจ ์ด๋ํผ์ง๋ณด๋ค ๋ ์ง๋ฐฐ์ ์ธ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ๋ํ ์ด ์ฐ๊ตฌ์์๋, ์์ง์๊ณผ ๊ด๋ จ๋ ์ผ์ฐจ ์ฒด์ฑ๊ฐ๊ฐํผ์ง์์์ ํ์ด-๊ฐ๋ง ๋ํ๋์ ๋ง์ด์ ๊ฒฝ๊ณ๋ก๋ถํฐ์ ์๊ธฐ์์ฉ๊ฐ๊ฐ๊ณผ ์ด๊ฐ์ ๋ํ ์ ๊ฒฝ๊ณ ์ ๋ณด์ฒ๋ฆฌ๋ฅผ ์ฃผ๋ก ๋ฐ์ํ๋ ๊ฒ์ ๋ฐํ๋ค.
์ด๋ฌํ ์ฐ๊ตฌ๋ค์ ๋ฐํ์ผ๋ก, ๋ง์ง๋ง ์ฐ๊ตฌ์์๋ ์ธ๊ฐ ๋๋์์์ ์ฒด์ฑ๊ฐ๊ฐ ์ง๊ฐ ํ๋ก์ธ์ค์ ๋ํ ๊ฑฐ์์ ํผ์ง ๊ฐ ๋คํธ์ํฌ๋ฅผ ๊ท๋ช
ํ๊ณ ์ ํ์๋ค. ์ด๋ฅผ ์ํด, 51๋ช
์ ๋์ ์ฆ ํ์์๊ฒ์ ์ฒด์ฑ๊ฐ๊ฐ์ ์ ๋ฐํ๋ ๋ํผ์ง์ ๊ธฐ์๊ทน ๋ฐ์ดํฐ์ 46๋ช
์ ํ์์๊ฒ์ ์ด๊ฐ๊ฐ ์๊ทน ๋ฐ ์ด๋ ์ํ ์ค์ ์ธก์ ํ ๋ํผ์ง๋ํ ํ์ด-๊ฐ๋ง ๋งคํ ๋ฐ์ดํฐ๋ฅผ ์ข
ํฉ์ ์ผ๋ก ๋ถ์ํ์๋ค. ๊ทธ ๊ฒฐ๊ณผ, ์ฒด์ฑ๊ฐ๊ฐ ์ง๊ฐ ํ๋ก์ธ์ค๋ ๋๋์์ ๋์ ์์ญ์ ๊ฑธ์ณ ๋ถํฌํ๋ ์ฒด์ฑ๊ฐ๊ฐ ๊ด๋ จ ๋คํธ์ํฌ์ ์ ๊ฒฝ ํ์ฑ์ ์๋ฐํ๋ค๋ ๊ฒ์ ์์๋๋ค. ๋ํ, ๋ํผ์ง์ ๊ธฐ์๊ทน์ ํตํ ๋๋ ์ง๋์ ํ์ด-๊ฐ๋ง ๋งคํ์ ํตํ ๋๋ ์ง๋๋ ์๋ก ์๋นํ ์ ์ฌ์ฑ์ ๋ณด์๋ค. ํฅ๋ฏธ๋กญ๊ฒ๋, ๋ํผ์ง์ ๊ธฐ์๊ทน๊ณผ ํ์ด-๊ฐ๋ง ํ๋์ ์ข
ํฉํ ๋์ง๋๋ค๋ก๋ถํฐ ์ฒด์ฑ๊ฐ๊ฐ ๊ด๋ จ ๋ ์์ญ์ ๊ณต๊ฐ์ ๋ถํฌ๊ฐ ์ฒด์ฑ๊ฐ๊ฐ ๊ธฐ๋ฅ์ ๋ฐ๋ผ ์๋ก ๋ฌ๋๊ณ , ๊ทธ์ ํด๋นํ๋ ๊ฐ ์์ญ๋ค์ ์๋ก ๋๋ ทํ๊ฒ ๋ค๋ฅธ ์๊ฐ์ ๋ค์ด๋๋ฏน์ค๋ฅผ ๊ฐ์ง๊ณ ์์ฐจ์ ์ผ๋ก ํ์ฑํ๋์๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ค์ ์ฒด์ฑ๊ฐ๊ฐ์ ๋ํ ๊ฑฐ์์ ์ ๊ฒฝ๊ณ ํ๋ก์ธ์ค๊ฐ ๊ทธ ์ง๊ฐ์ ๊ธฐ๋ฅ์ ๋ฐ๋ผ ๋๋ ท์ด ๋ค๋ฅธ ๊ณ์ธต์ ๋คํธ์ํฌ๋ฅผ ๊ฐ์ง๋ค๋ ์ ์ ์์ฌํ๋ค. ๋ ๋์๊ฐ, ๋ณธ ์ฐ๊ตฌ์์์ ๊ฒฐ๊ณผ๋ค์ ์ฒด์ฑ๊ฐ๊ฐ ์์คํ
์ ์ง๊ฐ-ํ๋ ๊ด๋ จ ์ ๊ฒฝํ๋ ํ๋ฆ์ ๊ดํ ์ด๋ก ์ ์ธ ๊ฐ์ค์ ๋ํ์ฌ ์ค๋๋ ฅ ์๋ ์ฆ๊ฑฐ๋ฅผ ์ ์ํ๊ณ ์๋ค.Tactile and proprioceptive perceptions are crucial for our daily life as well as survival. At the peripheral level, the transduction mechanisms and characteristics of mechanoreceptive afferents containing information required for these functions, have been well identified. However, our knowledge about the cortical processing mechanism for them in human is limited. The present series of studies addressed the macroscopic neural mechanism for perceptual processing of tactile and proprioceptive perception in human cortex.
In the first study, I investigated the macroscopic neural characteristics for various vibrotactile and texture stimuli including artificial and naturalistic ones in human primary and secondary somatosensory cortices (S1 and S2, respectively) using electrocorticography (ECoG). I found robust tactile frequency-specific high-gamma (HG, 50โ140 Hz) activities in both S1 and S2 with different temporal dynamics depending on the stimulus frequency. Furthermore, similar HG patterns of S1 and S2 were found in naturalistic stimulus conditions such as coarse/fine textures. These results suggest that human vibrotactile sensation involves macroscopic multi-regional hierarchical processing in the somatosensory system, even during the simplified stimulation.
In the second study, I tested whether the movement-related HG activities in parietal region mainly represent somatosensory feedback such as proprioception from periphery or primarily indicate cortico-cortical neural processing for movement preparation and control. I found that sensorimotor HG activities are more dominant in S1 than in M1 during voluntary movement. Furthermore, the results showed that movement-related HG activities in S1 mainly represent proprioceptive and tactile feedback from periphery.
Given the results of previous two studies, the final study aimed to identify the large-scale cortical networks for perceptual processing in human. To do this, I combined direct cortical stimulation (DCS) data for eliciting somatosensation and ECoG HG band (50 to 150 Hz) mapping data during tactile stimulation and movement tasks, from 51 (for DCS mapping) and 46 patients (for HG mapping) with intractable epilepsy. The results showed that somatosensory perceptual processing involves neural activation of widespread somatosensory-related network in the cortex. In addition, the spatial distributions of DCS and HG functional maps showed considerable similarity in spatial distribution between high-gamma and DCS functional maps. Interestingly, the DCS-HG combined maps showed distinct spatial distributions depending on the somatosensory functions, and each area was sequentially activated with distinct temporal dynamics. These results suggest that macroscopic neural processing for somatosensation has distinct hierarchical networks depending on the perceptual functions. In addition, the results of the present study provide evidence for the perception and action related neural streams of somatosensory system.
Throughout this series of studies, I suggest that macroscopic somatosensory network and structures of our brain are intrinsically organized by perceptual function and its purpose, not by somatosensory modality or submodality itself. Just as there is a purpose for human behavior, so is our brain.PART I. INTRODUCTION 1
CHAPTER 1: Somatosensory System 1
1.1. Mechanoreceptors in the Periphery 2
1.2. Somatosensory Afferent Pathways 4
1.3. Cortico-cortical Connections among Somatosensory-related Areas 7
1.4. Somatosensory-related Cortical Regions 8
CHAPTER 2: Electrocorticography 14
2.1. Intracranial Electroencephalography 14
2.2. High-Gamma Band Activity 18
CHAPTER 3: Purpose of This Study 24
PART II. EXPERIMENTAL STUDY 26
CHAPTER 4: Apparatus Design 26
4.1. Piezoelectric Vibrotactile Stimulator 26
4.2. Magnetic Vibrotactile Stimulator 29
4.3. Disc-type Texture Stimulator 33
4.4. Drum-type Texture Stimulator 36
CHAPTER 5: Vibrotactile and Texture Study 41
5.1. Introduction 42
5.2. Materials and Methods 46
5.2.1. Patients 46
5.2.2. Apparatus 47
5.2.3. Experimental Design 49
5.2.4. Data Acquisition and Preprocessing 50
5.2.5. Analysis 51
5.3. Results 54
5.3.1. Frequency-specific S1/S2 HG Activities 54
5.3.2. S1 HG Attenuation during Flutter and Vibration 62
5.3.3. Single-trial Vibration Frequency Classification 64
5.3.4. S1/S2 HG Activities during Texture Stimuli 65
5.4. Discussion 69
5.4.1. Comparison with Previous Findings 69
5.4.2. Tactile Frequency-dependent Neural Adaptation 70
5.4.3. Serial vs. Parallel Processing between S1 and S2 72
5.4.4. Conclusion of Chapter 5 73
CHAPTER 6: Somatosensory Feedback during Movement 74
6.1. Introduction 75
6.2. Materials and Methods 79
6.2.1. Subjects 79
6.2.2. Tasks 80
6.2.3. Data Acquisition and Preprocessing 82
6.2.4. S1-M1 HG Power Difference 85
6.2.5. Classification 86
6.2.6. Timing of S1 HG Activity 86
6.2.7. Correlation between HG and EMG signals 87
6.3. Results 89
6.3.1. HG Activities Are More Dominant in S1 than in M1 89
6.3.2. HG Activities in S1 Mainly Represent Somatosensory Feedback 94
6.4. Discussion 100
6.4.1. S1 HG Activity Mainly Represents Somatosensory Feedback 100
6.4.2. Further Discussion and Future Direction in BMI 102
6.4.3. Conclusion of Chapter 6 103
CHAPTER 7: Cortical Maps of Somatosensory Function 104
7.1. Introduction 106
7.2. Materials and Methods 110
7.2.1. Participants 110
7.2.2. Direct Cortical Stimulation 114
7.2.3. Classification of Verbal Feedbacks 115
7.2.4. Localization of Electrodes 115
7.2.5. Apparatus 116
7.2.6. Tasks 117
7.2.7. Data Recording and Processing 119
7.2.8. Mapping on the Brain 120
7.2.9. ROI-based Analysis 122
7.3. Results 123
7.3.1. DCS Mapping 123
7.3.2. Three and Four-dimensional HG Mapping 131
7.3.3. Neural Characteristics among Somatosensory-related Areas 144
7.4. Discussion 146
7.4.1. DCS on the Non-Primary Areas 146
7.4.2. Two Streams of Somatosensory System 148
7.4.3. Functional Role of ventral PM 151
7.4.4. Limitation and Perspective 152
7.4.5. Conclusion of Chapter 7 155
PART III. CONCLUSION 156
CHAPTER 8: Conclusion and Perspective 156
8.1. Perspective and Future Work 157
References 160
Abstract in Korean 173Docto
Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A
2019 Spring.Includes bibliographical references.The electroencephalogram (EEG) is broadly used for diagnosis of brain diseases and research of brain activities. Although the EEG provides a good temporal resolution, it suffers from poor spatial resolution due to the blurring effects of volume conduction and signal-to-noise ratio. Many efforts have been devoted to the development of novel methods that can increase the EEG spatial resolution. The surface Laplacian, which is the second derivative of the surface potential, has been applied to EEG to improve the spatial resolution. Tri-polar concentric ring electrodes (TCREs) have been shown to estimate the surface Laplacian automatically with better spatial resolution than conventional disc electrodes. The aim of this research is to study how well the TCREs can be used to acquire EEG signals to decode real and imaginary finger movements. These EEG signals will be then translated into finger movements commands. We also compare the feasibility of discriminating finger movements from one hand using EEG recorded from TCREs and conventional disc electrodes. Furthermore, we evaluated two movement-related features, temporal EEG data and spectral features, in discriminating individual finger from one hand using non-invasive EEG. To do so, movement-related potentials (MRPs) are measured and analyzed from four TCREs and conventional disc electrodes while 13 subjects performed either motor execution or motor imagery of individual finger movements. The tri-polar-EEG (tEEG) and conventional EEG (cEEG) were recorded from electrodes placed according to the 10-20 International Electrode Positioning System over the motor cortex. Our results show that the TCREs achieved higher spatial resolution than conventional disc electrodes. Moreover, the results show that signals from TCREs generated higher decoding accuracy compared to signals from conventional disc electrodes. The average decoding accuracy of five-class classification for all subjects was of 70.04 ยฑ 7.68% when we used temporal EEG data as feature and classified it using Artificial Neural Networks (ANNs) classifier. In addition, the results show that the TCRE EEG (tEEG) provides approximately a four times enhancement in the signal-to-noise ratio (SNR) compared to disc electrode signals. We also evaluated the interdependency level between neighboring electrodes from tri-polar, disc, and disc with Hjorth's Laplacian method in time and frequency domains by calculating the mutual information (MI) and coherence. The MRP signals recorded with the TCRE system have significantly less mutual information (MI) between electrodes than the conventional disc electrode system and disc electrodes with Hjorth's Laplacian method. Also, the results show that the mean coherence between neighboring tri-polar electrodes was found to be significantly smaller than disc electrode and disc electrode with Hjorth's method, especially at higher frequencies. This lower coherence in the high frequency band between neighboring tri polar electrodes suggests that the TCREs may record a more localized neuronal activity. The successful decoding of finger movements can provide extra degrees of freedom to drive brain computer interface (BCI) applications, especially for neurorehabilitation
A Python-based Brain-Computer Interface Package for Neural Data Analysis
Anowar, Md Hasan, A Python-based Brain-Computer Interface Package for Neural Data Analysis. Master of Science (MS), December, 2020, 70 pp., 4 tables, 23 figures, 74 references.
Although a growing amount of research has been dedicated to neural engineering, only a handful of software packages are available for brain signal processing. Popular brain-computer interface packages depend on commercial software products such as MATLAB. Moreover, almost every brain-computer interface software is designed for a specific neuro-biological signal; there is no single Python-based package that supports motor imagery, sleep, and stimulated brain signal analysis. The necessity to introduce a brain-computer interface package that can be a free alternative for commercial software has motivated me to develop a toolbox using the python platform. In this thesis, the structure of MEDUSA, a brain-computer interface toolbox, is presented. The features of the toolbox are demonstrated with publicly available data sources. The MEDUSA toolbox provides a valuable tool to biomedical engineers and computational neuroscience researchers
Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data
High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures
ECoG correlates of visuomotor transformation, neural plasticity, and application to a force-based brain computer interface
Electrocorticography: ECoG) has gained increased notoriety over the past decade as a possible recording modality for Brain-Computer Interface: BCI) applications that offers a balance of minimal invasiveness to the patient in addition to robust spectral information over time. More recently, the scale of ECoG devices has begun to shrink to the order of micrometer diameter contacts and millimeter spacings with the intent of extracting more independent signals for BCI control within less cortical real-estate. However, most control signals to date, whether within the field of ECoG or any of the more seasoned recording techniques, have translated their control signals to kinematic control parameters: i.e. position or velocity of an object) which may not be practical for certain BCI applications such as functional neuromuscular stimulation: FNS). Thus, the purpose of this dissertation was to present a novel application of ECoG signals to a force-based control algorithm and address its feasibility for such a BCI system. Micro-ECoG arrays constructed from thin-film polyimide were implanted epidurally over areas spanning premotor, primary motor, and parietal cortical areas of two monkeys: three hemispheres, three arrays). Monkeys first learned to perform a classic center-out task using a brain signal-to-velocity mapping for control of a computer cursor. The BCI algorithm utilized day-to-day adaptation of the decoding model to match the task intention of the monkeys with no need for pre-screeening of movement-related ECoG signals. Using this strategy, subjects showed notable 2-D task profiency and increased task-related modulation of ECoG features within five training sessions. After fixing the last model trained for velocity control of the cursor, the monkeys then utilized this decoding model to control the acceleration of the cursor in the same center-out task. Cursor movement profiles under this mapping paralleled those demonstrated using velocity control, and neural control signal profiles revealed the monkeys actively accelerated and decelerated the cursor within a limited time window: 1-1.5 seconds). The fixed BCI decoding model was recast once again to control the force on a virtual cursor in a novel mass-grab task. This task required targets not only to reach to peripheral targets but also account for an additional virtual mass as they grabbed each target and moved it to a second target location in the presence of the external force of gravity. Examination of the ensemble control signals showed neural adaptation to variations in the perceived mass of the target as well as the presence or absence of gravity. Finally, short rest periods were interleaved within blocks of each task type to elucidate differences between active BCI intention and rest. Using a post-hoc state-decoder model, periods of active BCI task control could be distinguished from periods of rest with a very high degree of accuracy: ~99%). Taken together, the results from these experiments present a first step toward the design of a dynamics-based BCI system suitable for FNS applications as well as a framework for implementation of an asyncrhonous ECoG BCI
Restoring Fine Motor Skills through Neural Interface Technology.
Loss of motor function in the upper-limb, whether through paralysis or through loss of the limb itself, is a profound disability which affects a large population worldwide. Lifelike, fully-articulated prosthetic hands exist and are commercially available; however, there is currently no satisfactory method of controlling all of the available degrees of freedom. In order to generate better control signals for this technology, and help restore normal movement, it is necessary to interface directly with the nervous system. This thesis is intended to address several of the limitations of current neural interfaces and enable the long-term extraction of control signals for fine movements of the hand and fingers.
The first study addresses the problems of low signal amplitudes and short implant lifetimes in peripheral nerve interfaces. In two rhesus macaques, we demonstrate the successful implantation of regenerative peripheral nerve interfaces (RPNI), which allowed us to record high amplitude, functionally-selective signals from peripheral nerves up to 20 months post-implantation. These signals could be accurately decoded into intended movement, and used to enable monkeys to control a virtual hand prosthesis.
The second study presents a novel experimental paradigm for intracortical neural interfaces, which enables detailed investigation of fine motor information contained in primary motor cortex. We used this paradigm to demonstrate accurate decoding of continuous fingertip position and enable a monkey to control a virtual hand in closed-loop. This is the first demonstration of volitional control of fine motor skill enabled by a cortical neural interface.
The final study presents the design and testing of a wireless implantable neural recording system. By extracting signal power in a single, configurable frequency band onboard the device, this system achieves low power consumption while maintaining decode performance, and is applicable to cortical, peripheral, and myoelectric signals.
Taken together, these results represent a significant step towards clinical reality for neural interfaces, and towards restoration of full and dexterous movement for people with severe disabilities.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120648/1/irwinz_1.pd
Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI
There are significant milestones in modern human's civilization in which
mankind stepped into a different level of life with a new spectrum of
possibilities and comfort. From fire-lighting technology and wheeled wagons to
writing, electricity and the Internet, each one changed our lives dramatically.
In this paper, we take a deep look into the invasive Brain Machine Interface
(BMI), an ambitious and cutting-edge technology which has the potential to be
another important milestone in human civilization. Not only beneficial for
patients with severe medical conditions, the invasive BMI technology can
significantly impact different technologies and almost every aspect of human's
life. We review the biological and engineering concepts that underpin the
implementation of BMI applications. There are various essential techniques that
are necessary for making invasive BMI applications a reality. We review these
through providing an analysis of (i) possible applications of invasive BMI
technology, (ii) the methods and devices for detecting and decoding brain
signals, as well as (iii) possible options for stimulating signals into human's
brain. Finally, we discuss the challenges and opportunities of invasive BMI for
further development in the area.Comment: 51 pages, 14 figures, review articl
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