Movements Of Different Fingers Research Articles

ПРИМЕНЕНИЕ МЕТОДА ХИГУЧИ ДЛЯ АНАЛИЗА ЭЛЕКТРОМИОГРАММЫ

The application of the fractal Higuchi method to the analysis of electromyograms (EMG) is considered. It is shown that the application of the method makes it possible to see the difference between the movements of different fingers by EMG recorded by surface electrodes from the human forearm in characteristic zones

Machine Learning-Based Framework to Predict Finger Movement for Prosthetic Hand

Loss of limb is critical for human life. The prosthetic hand can be an alternate for biological hand up to some extent although it can never mimic the original. This letter presents a machine learning (ML) framework to detect finger movement for the prosthetic hand. The data generated corresponding to different movements of different fingers make the classification problem as multiclass. To address this problem, four ML-based classifiers—K-nearest neighbors, decision tree, random forest, and extreme gradient boosting—have been employed. These classifiers possess inherent property of handling the multiclass classification problem. The employed classifiers have been evaluated for finger movement prediction with respect to precision, recall, F1-score, and accuracy. The experiments show that the XGBoost classifier outperformed the other multiclass classifiers in terms of accuracy.

ПРИМЕНЕНИЕ МЕТОДА ХИГУЧИ ДЛЯ АНАЛИЗА ЭЛЕКТРОМИОГРАММЫ

The application of the fractal Higuchi method to the analysis of electromyograms (EMG) is considered. It is shown that the application of the method makes it possible to see the difference between the movements of different fingers by EMG recorded by surface electrodes from the human forearm in characteristic zones

Structure of Population Activity in Primary Motor Cortex for Single Finger Flexion and Extension.

How is the primary motor cortex (M1) organized to control fine finger movements? We investigated the population activity in M1 for single finger flexion and extension, using 7T functional magnetic resonance imaging (fMRI) in female and male human participants and compared these results to the neural spiking patterns recorded in two male monkeys performing the identical task. fMRI activity patterns were distinct for movements of different fingers, but were quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, which is similar to what was found for muscular activity patterns. The discrepancy between fMRI and electrophysiological measurements can be explained by two (non-mutually exclusive) characteristics of the organization of finger flexion and extension movements. Given that fMRI reflects predominantly input and recurrent activity, the results can be explained by an architecture in which neural populations that control flexion or extension of the same finger produce distinct outputs, but interact tightly with each other and receive similar inputs. Additionally, neurons tuned to different movement directions for the same finger (or combination of fingers) may cluster closely together, while neurons that control different finger combinations may be more spatially separated. When measuring this organization with fMRI at a coarse spatial scale, the activity patterns for flexion and extension of the same finger would appear very similar. Overall, we suggest that the discrepancy between fMRI and electrophysiological measurements provides new insights into the general organization of fine finger movements in M1.SIGNIFICANCE STATEMENT The primary motor cortex (M1) is important for producing individuated finger movements. Recent evidence shows that movements that commonly co-occur are associated with more similar activity patterns in M1. Flexion and extension of the same finger, which never co-occur, should therefore be associated with distinct representations. However, using carefully controlled experiments and multivariate analyses, we demonstrate that human fMRI activity patterns for flexion or extension of the same finger are highly similar. In contrast, spiking patterns measured in monkey M1 are clearly distinct. This suggests that populations controlling opposite movements of the same finger, while producing distinct outputs, may cluster together and share inputs and local processing. These results provide testable hypotheses about the organization of hand control in M1.

EEG-based BCI system for decoding finger movements within the same hand

Decoding the movements of different fingers within the same hand can increase the control's dimensions of the electroencephalography (EEG)-based brain–computer interface (BCI) systems. This in turn enables the subjects who are using assistive devices to better perform various dexterous tasks. However, decoding the movements performed by different fingers within the same hand by analyzing the EEG signals is considered a challenging task. In this paper, we present a new EEG-based BCI system for decoding the movements of each finger within the same hand based on analyzing the EEG signals using a quadratic time-frequency distribution (QTFD), namely the Choi–William distribution (CWD). In particular, the CWD is employed to characterize the time-varying spectral components of the EEG signals and extract features that can capture movement-related information encapsulated within the EEG signals. The extracted CWD-based features are used to build a two-layer classification framework that decodes finger movements within the same hand. The performance of the proposed system is evaluated by recording the EEG signals for eighteen healthy subjects while performing twelve finger movements using their right hands. The results demonstrate the efficacy of the proposed system to decode finger movements within the same hand of each subject.

EEG resolutions in detecting and decoding finger movements from spectral analysis.

Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g., hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA) was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%). More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements), which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of non-invasive BCI and neuroprosthesis with intuitive and flexible controls.

Motor cortex and the distributed anatomy of finger movements.

Voluntary movements are thought to be controlled via a well-ordered, spatially discrete, somatotopic map in the primary motor cortex (M1). We examined this hypothesis in monkeys trained to perform visually-cued, individuated flexion and extension movements of each digit and of the wrist. Single neurone recordings in M1 during such finger movements revealed two unexpected features. First, single M1 neurones often discharge during instructed movements of multiple digits. Second, neurones active during any particular instructed movement are distributed widely throughout the same M1 territory as neurones active during any other movement. Reversible, partial inactivation of the M1 hand representation produced by injection of 5-10 microg muscimol at one site impaired the monkeys' ability to perform finger movements, but no relationship was evident between the particular finger movements that were affected and the mediolateral location of the injection site along the central sulcus. Thus each finger movement is represented by activity distributed widely in the M1 upper extremity representation. If not controlled from spatially segregated M1 regions, movements of different fingers might be controlled by groups of spatially scattered but physiologically similar neurones. Cluster analysis of M1 neurones demonstrated a large group that discharged during most finger movements, and a small group that paused during most movements. Distinct functional groups of M1 neurones that might control particular finger movements were identified inconsistently. We therefore hypothesize that M1 neurones are a very diverse network controlling finger movements.

Neural coding of finger and wrist movements.

Previous work (Schieber and Hibbard, 1993) has shown that single motor cortical neurons do not discharge specifically for a particular flexion-extension finger movement but instead are active with movements of different fingers. In addition, neuronal populations active with movements of different fingers overlap extensively in their spatial locations in the motor cortex. These data suggested that control of any finger movement utilizes a distributed population of neurons. In this study we applied the neuronal population vector analysis (Georgopoulos et al., 1983) to these same data to determine (1) whether single cells are tuned in an abstract, three-dimensional (3D) instructed finger and wrist movement space with hand-like geometry and (2) whether the neuronal population encodes specific finger movements. We found that the activity of 132/176 (75%) motor cortical neurons related to finger movements was indeed tuned in this space. Moreover, the population vector computed in this space predicted well the instructed finger movement. Thus, although single neurons may be related to several disparate finger movements, and neurons related to different finger movements are intermingled throughout the hand area of the motor cortex, the neuronal population activity does specify particular finger movements.

Muscular production of individuated finger movements: the roles of extrinsic finger muscles

Individuated finger movements--those in which one or more fingers are moved relatively independently of the movement or posture of other body parts--are produced in part by the action of the extrinsic finger muscles. Flexion/extension movements of the fingers are particularly dependent on these extrinsic muscles, most of which are multitendoned. How can contraction of multitendoned muscles move one digit without producing equivalent motion in other digits? This question was addressed by recording EMG activity from muscles of the forearm as trained rhesus monkeys performed flexion and extension individuated movements of each digit of the hand and of the wrist. Recordings showed that during movements of different fingers, a given muscle could act as an agonist, antagonist, or stabilizer of the digits it serves. Furthermore, during a given finger movement, several different muscles typically were active. A three-level connection model was developed that computed the relative motion of the digits during each finger movement based on the changes in EMG activity in the recorded muscles. The model showed that EMG activity changes in the extrinsic finger muscles, and the thenar muscles, could account for most of the motion of both the instructed digit and noninstructed digits. These results indicate that individuated finger movements were produced not by independent sets of muscles acting on each digit, but by the activity of several muscles, many of which act on more than one digit, combined such that the net effect was movement of one digit more than others.

How somatotopic is the motor cortex hand area?

The primary motor cortex (M1) is thought to control movements of different body parts from somatotopically organized cortical territories. Electrical stimulation suggests, however, that territories controlling different fingers overlap. Such overlap might be artifactual or else might indicate that activation of M1 to produce a finger movement occurs over a more widespread cortical area than usually assumed. These possibilities were distinguished in monkeys moving different fingers. Recordings showed that single M1 neurons were active with movements of different fingers. Neuronal populations active with movements of different fingers overlapped extensively. Control of any finger movement thus appears to utilize a population of neurons distributed throughout the M1 hand area rather than a somatotopically segregated population.