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Durán-Santos M, Salazar-Varas R, Etcheverry G. Modeling the cortical response elicited by wrist manipulation via a nonlinear delay differential embedding. Phys Eng Sci Med 2024; 47:1-14. [PMID: 38739346 DOI: 10.1007/s13246-024-01427-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/16/2024] [Indexed: 05/14/2024]
Abstract
Regarding motor processes, modeling healthy people's brains is essential to understand the brain activity in people with motor impairments. However, little research has been undertaken when external forces disturb limbs, having limited information on physiological pathways. Therefore, in this paper, a nonlinear delay differential embedding model is used to estimate the brain response elicited by externally controlled wrist movement in healthy individuals. The aim is to improve the understanding of the relationship between a controlled wrist movement and the generated cortical activity of healthy people, helping to disclose the underlying mechanisms and physiological relationships involved in the motor event. To evaluate the model, a public database from the Delft University of Technology is used, which contains electroencephalographic recordings of ten healthy subjects while wrist movement was externally provoked by a robotic system. In this work, the cortical response related to movement is identified via Independent Component Analysis and estimated based on a nonlinear delay differential embedding model. After a cross-validation analysis, the model performance reaches 90.21% ± 4.46% Variance Accounted For, and Correlation 95.14% ± 2.31%. The proposed methodology allows to select the model degree, to estimate a general predominant operation mode of the cortical response elicited by wrist movement. The obtained results revealed two facts that had not previously been reported: the movement's acceleration affects the cortical response, and a common delayed activity is shared among subjects. Going forward, identifying biomarkers related to motor tasks could aid in the evaluation of rehabilitation treatments for patients with upper limbs motor impairments.
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Affiliation(s)
- Martín Durán-Santos
- Department of Computing, Electronics and Mechatronics, Universidad de las Americas Puebla (UDLAP), Ex Hacienda Sta. Catarina Mártir S/N, C.P. 72810, San Andrés Cholula, Puebla, Mexico.
| | - R Salazar-Varas
- Department of Computing, Electronics and Mechatronics, Universidad de las Americas Puebla (UDLAP), Ex Hacienda Sta. Catarina Mártir S/N, C.P. 72810, San Andrés Cholula, Puebla, Mexico
| | - Gibran Etcheverry
- Department of Mathematics, Tiffin University, 155 Miami St, Tiffin, OH, 44883, USA
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Martin del Campo Vera R, Sundaram S, Lee R, Lee Y, Leonor A, Chung RS, Shao A, Cavaleri J, Gilbert ZD, Zhang S, Kammen A, Mason X, Heck C, Liu CY, Kellis S, Lee B. Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus. J Neural Eng 2024; 21:046017. [PMID: 38914073 PMCID: PMC11247508 DOI: 10.1088/1741-2552/ad5b19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/25/2024] [Accepted: 06/24/2024] [Indexed: 06/26/2024]
Abstract
Objective.Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between 'Go' and 'No-go' trials in a Go/No-go arm-reaching task.Approach.Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a 'center-out' Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between 'Go' and 'No-go' trials. The accuracy of five different discriminant functions was evaluated using cross-validation.Main results.The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants.Significance.This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface applications.
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Affiliation(s)
- Roberto Martin del Campo Vera
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Shivani Sundaram
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Richard Lee
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Yelim Lee
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Andrea Leonor
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Ryan S Chung
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Arthur Shao
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Jonathon Cavaleri
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Zachary D Gilbert
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Selena Zhang
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Alexandra Kammen
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Xenos Mason
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Christi Heck
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Spencer Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Brian Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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Behmer LP. Mu-ERD reflects action understanding, but the effect is small. Brain Res 2024; 1832:148854. [PMID: 38493572 DOI: 10.1016/j.brainres.2024.148854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/05/2024] [Accepted: 03/07/2024] [Indexed: 03/19/2024]
Abstract
Since the mid-2000's, many researchers have provided evidence that mu-ERD measured at the motor cortex may reflect the collective activation of upstream brain regions associated with the human mirror system during action observation paradigms; however, several recent papers have called these findings into question. Our study represents an effort to address these criticisms. In our study, participants watched videos in which the type of grip an actor used to grasp a coffee mug either conveyed the goal with 100 % certainty (unambiguous-goal trials), or offered no predictive information (ambiguous-goal trials). If mu-ERD indexes action understanding, then we predicted that mu-ERD should increase while participants watched the actor grasp the mug for unambiguous-goal trials, but not for ambiguous-goal trials. During the intervals where participants watched the actor execute the goal, mu-ERD for unambiguous-goal trials should remain steady, whereas mu-ERD for ambiguous-goal trials should now increase. Conversely, if mu-ERD does not index action understanding, and instead reflects general motor processes associated with action (such as the activation of population vectors in M1 or planning processes), then mu-ERD should show no difference across conditions. Across most comparisons, we found that mu-ERD mostly reflected general motor processes; however, there was a small effect when participants overserved unambiguous-goal trials while watching the actor execute the goal suggesting that mu-ERD does reflect mirroring, but the effect is small.
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Domic-Siede M, Sánchez-Corzo A, Guzmán-González M. Brain oscillations during emotion regulation and the two-dimensional model of adult attachment. Biol Psychol 2024; 189:108793. [PMID: 38631550 DOI: 10.1016/j.biopsycho.2024.108793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/26/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024]
Abstract
Emotion Regulation (ER) refers to the processes by which individuals influence their own emotions. It is a crucial aspect of human behavior, affecting everything from interpersonal relationships to mental health. The relationship between ER and Attachment Theory (AT) is pivotal. AT suggests that early bonds with primary caregivers influence future relationship expectations and behaviors. These initial experiences shape internal models of self and others, affecting how individuals regulate their emotions. Understanding the interplay between ER and AT is essential for comprehending the human affective system. In this study, we explored the neural underpinnings of ER, focusing on two distinct strategies: cognitive reappraisal and expressive suppression. Using electroencephalography (EEG), we examined changes in neural oscillations from 52 adults during an ER task. Specifically, we observed increased frontal theta activity (3-6 Hz) during reappraisal compared to suppression strategies. This frontal theta activity suggests enhanced cognitive control engagement. Conversely, during suppression, we noted a decrease in beta frequency (15-30 Hz) activity from central electrodes, indicative of differing neural processes. Further integrating psychological theories, we explored the relationship between these neural markers and dimensions of human attachment. Employing the Experiences in Close Relationships-12 scale (ECR-12), we identified a negative correlation between attachment anxiety and frontal theta activity. Lower levels of attachment anxiety were associated with increased theta activity, reflecting potentially more effective emotion regulation. Additionally, we found that higher theta activity corresponded with fewer difficulties in emotional control measured by the Difficulties in Emotion Regulation Scale (DERS). Regarding central beta activity, our findings revealed an interesting correlation with Emotional Inattention, a concept tied to Attachment Avoidance. This suggests that central beta activity may serve as a neural marker for specific attachment-related ER processing. These results highlight the distinct neural pathways involved in different ER strategies and their relationship with the AT and neural responses during emotional processing.
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Affiliation(s)
- Marcos Domic-Siede
- Laboratorio de Neurociencia Cognitiva, Escuela de Psicología, Universidad Católica del Norte, Antofagasta, Chile.
| | - Andrea Sánchez-Corzo
- Multimodal Functional Brain Imaging and Neurorehabilitation Hub, Department of Diagnostic Imaging. St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Mónica Guzmán-González
- Laboratorio de Neurociencia Cognitiva, Escuela de Psicología, Universidad Católica del Norte, Antofagasta, Chile
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Merino EC, Faes A, Van Hulle MM. The role of distinct ECoG frequency features in decoding finger movement. J Neural Eng 2023; 20:066014. [PMID: 37963397 DOI: 10.1088/1741-2552/ad0c5e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective.To identify the electrocorticography (ECoG) frequency features that encode distinct finger movement states during repeated finger flexions.Approach.We used the publicly available Stanford ECoG dataset of cue-based, repeated single finger flexions. Using linear regression, we identified the spectral features that contributed most to the encoding of movement dynamics and discriminating movement events from rest, and combined them to predict finger movement trajectories. Furthermore, we also looked into the effect of the used frequency range and the spatial distribution of the identified features.Main results.Two frequency features generate superior performance, each one for a different movement aspect: high gamma band activity distinguishes movement events from rest, whereas the local motor potential (LMP) codes for movement dynamics. Combining these two features in a finger movement decoder outperformed comparable prior work where the entire spectrum was used as the average correlation coefficient with the true trajectories increased from 0.45 to 0.5, both applied to the Stanford dataset, and erroneous predictions during rest were demoted. In addition, for the first time, our results show the influence of the upper cut-off frequency used to extract LMP, yielding a higher performance when this range is adjusted to the finger movement rate.Significance.This study shows the benefit of a detailed feature analysis prior to designing the finger movement decoder.
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Affiliation(s)
- Eva Calvo Merino
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
| | - A Faes
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
| | - M M Van Hulle
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
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Xia Y, Hua L, Dai Z, Han Y, Du Y, Zhao S, Zhou H, Wang X, Yan R, Wang X, Zou H, Sun H, Huang Y, Yao Z, Lu Q. Attenuated post-movement beta rebound reflects psychomotor alterations in major depressive disorder during a simple visuomotor task: a MEG study. BMC Psychiatry 2023; 23:395. [PMID: 37270511 DOI: 10.1186/s12888-023-04844-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 05/04/2023] [Indexed: 06/05/2023] Open
Abstract
BACKGROUND Psychomotor alterations are a common symptom in patients with major depressive disorder (MDD). The primary motor cortex (M1) plays a vital role in the mechanism of psychomotor alterations. Post-movement beta rebound (PMBR) in the sensorimotor cortex is abnormal in patients with motor abnormalities. However, the changes in M1 beta rebound in patients with MDD remain unclear. This study aimed to primarily explore the relationship between psychomotor alterations and PMBR in MDD. METHODS One hundred thirty-two subjects were enrolled in the study, comprising 65 healthy controls (HCs) and 67 MDD patients. All participants performed a simple right-hand visuomotor task during MEG scanning. PMBR was measured in the left M1 at the source reconstruction level with the time-frequency analysis method. Retardation factor scores and neurocognitive test performance, including the Digit Symbol Substitution Test (DSST), the Making Test Part A (TMT-A), and the Verbal Fluency Test (VFT), were used to measure psychomotor functions. Pearson correlation analyses were used to assess relationships between PMBR and psychomotor alterations in MDD. RESULTS The MDD group showed worse neurocognitive performance than the HC group in all three neurocognitive tests. The PMBR was diminished in patients with MDD compared to HCs. In a group of MDD patients, the reduced PMBR was negatively correlated with retardation factor scores. Further, there was a positive correlation between the PMBR and DSST scores. PMBR is negatively associated with the TMT-A scores. CONCLUSION Our findings suggested that the attenuated PMBR in M1 could illustrate the psychomotor disturbance in MDD, possibly contributing to clinical psychomotor symptoms and deficits of cognitive functions.
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Affiliation(s)
- Yi Xia
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Lingling Hua
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Yinglin Han
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yishan Du
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shuai Zhao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hongliang Zhou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xiaoqin Wang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Xumiao Wang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - HaoWen Zou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Hao Sun
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - YingHong Huang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - ZhiJian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, 210096, China.
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Kurkin S, Gordleeva S, Savosenkov A, Grigorev N, Smirnov N, Grubov VV, Udoratina A, Maksimenko V, Kazantsev V, Hramov AE. Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery. SENSORS (BASEL, SWITZERLAND) 2023; 23:4661. [PMID: 37430576 DOI: 10.3390/s23104661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 07/12/2023]
Abstract
Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study. Participants were randomly assigned to receive sham (15 subjects) or real high-frequency rTMS (15 subjects). We performed EEG sensor-level, source-level, and connectivity analyses to evaluate the rTMS effects. We revealed that excitatory stimulation of the left DLPFC increases theta-band power in the right precuneus (PrecuneusR) via the functional connectivity between them. The precuneus theta-band power negatively correlates with the latency of the MI response, so the rTMS speeds up the responses in 50% of participants. We suppose that posterior theta-band power reflects attention modulation of sensory processing; therefore, high power may indicate attentive processing and cause faster responses.
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Affiliation(s)
- Semen Kurkin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Susanna Gordleeva
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Andrey Savosenkov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Nikita Grigorev
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Nikita Smirnov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Vadim V Grubov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Anna Udoratina
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Vladimir Maksimenko
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Victor Kazantsev
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Alexander E Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
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Lee HS, Schreiner L, Jo SH, Sieghartsleitner S, Jordan M, Pretl H, Guger C, Park HS. Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system. Front Neurosci 2022; 16:1009878. [PMID: 36340769 PMCID: PMC9627315 DOI: 10.3389/fnins.2022.1009878] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.
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Affiliation(s)
- Hyemin S. Lee
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Leonhard Schreiner
- g.tec Medical Engineering GmbH, Schiedlberg, Upper Austria, Austria
- Institute for Integrated Circuits, Johannes Kepler University, Linz, Austria
| | - Seong-Hyeon Jo
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | | | - Michael Jordan
- g.tec Medical Engineering GmbH, Schiedlberg, Upper Austria, Austria
| | - Harald Pretl
- Institute for Integrated Circuits, Johannes Kepler University, Linz, Austria
| | - Christoph Guger
- g.tec Medical Engineering GmbH, Schiedlberg, Upper Austria, Austria
| | - Hyung-Soon Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
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9
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Kim S, Shin DY, Kim T, Lee S, Hyun JK, Park SM. Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography. SENSORS 2022; 22:s22020680. [PMID: 35062641 PMCID: PMC8778369 DOI: 10.3390/s22020680] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/09/2022] [Accepted: 01/14/2022] [Indexed: 02/04/2023]
Abstract
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18-4.35% in the control group, and by 2.51-3.00% in the patient group.
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Affiliation(s)
- Sehyeon Kim
- Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea;
| | - Dae Youp Shin
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea;
| | - Taekyung Kim
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 03063, Korea;
| | - Sangsook Lee
- Department of Rehabilitation Medicine, Daejeon Hospital, Daejeon 34383, Korea;
| | - Jung Keun Hyun
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea;
- Department of Nanobiomedical Science & BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Korea
- Institute of Tissue Regeneration Engineering (ITREN), Dankook University, Cheonan 31116, Korea
- Correspondence: (J.K.H.); (S.-M.P.); Tel.: +82-10-2293-3415 (J.K.H.); +82-10-7208-7740 (S.-M.P.)
| | - Sung-Min Park
- Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea;
- Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Correspondence: (J.K.H.); (S.-M.P.); Tel.: +82-10-2293-3415 (J.K.H.); +82-10-7208-7740 (S.-M.P.)
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