Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks

 Maybe, if you're lucky your doctor can figure out how to translate this from healthy subjects to your needs and get you recovered. Since both my motor and pre-motor cortex is dead there is no possibility of BCI ever working for me.

Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks

• Qiang Sun,
• Eva Calvo Merino,
• Liuyin Yang &
• Marc M. Van Hulle 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 228 (2024) Cite this article

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Abstract

Background

The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved. This study aims to investigate the EEG correlates of unimanual, non-repetitive finger flexion and extension.

Methods

Sixteen healthy, right-handed participants completed multiple sessions of right-hand finger movement experiments. These included five individual (Thumb, Index, Middle, Ring, and Pinky) and four coordinated (Pinch, Point, ThumbsUp, and Fist) finger flexions and extensions, along with a rest condition (None). High-density EEG and finger trajectories were simultaneously recorded and analyzed. We examined low-frequency (0.3–3 Hz) time series and movement-related cortical potentials (MRCPs), and event-related desynchronization/synchronization (ERD/S) in the alpha- (8–13 Hz) and beta (13–30 Hz) bands. A clustering approach based on Riemannian distances was used to chart similarities between the broadband EEG responses (0.3–70 Hz) to the different finger scenarios. The contribution of different state-of-the-art features was identified across sub-bands, from low-frequency to low gamma (30–70 Hz), and an ensemble approach was used to pairwise classify single-trial finger movements and rest.

Results

A significant decrease in EEG amplitude in the low-frequency time series was observed in the contralateral frontal-central regions during finger flexion and extension. Distinct MRCP patterns were found in the pre-, ongoing-, and post-movement stages. Additionally, strong ERD was detected in the contralateral central brain regions in both alpha and beta bands during finger flexion and extension, with the beta band showing a stronger rebound (ERS) post-movement. Within the finger movement repertoire, the Thumb was most distinctive, followed by the Fist. Decoding results indicated that low-frequency time-domain amplitude better differentiates finger movements, while alpha and beta band power and Riemannian features better detect movement versus rest. Combining these features yielded over 80% finger movement detection accuracy, while pairwise classification accuracy exceeded 60% for the Thumb versus the other fingers.

Conclusion

Our findings confirm that non-repetitive finger movements, whether individual or coordinated, can be precisely detected from EEG. However, differentiating between specific movements is challenging due to highly overlapping neural correlates in time, spectral, and spatial domains. Nonetheless, certain finger movements, such as those involving the Thumb, exhibit distinct EEG responses, making them prime candidates for dexterous finger neuroprostheses.

Background

Individuals with neuromuscular disorders often experience significant losses in hand strength, tone, movement, dexterity, joint range, and sensation, severely impacting their quality of life [1]. One promising technology for addressing these challenges is a motor brain-computer interface (BCI), the purpose of which is to decode motor intentions from the brain to directly control end effectors [2, 3]. For example, Hotson et al. successfully decoded individual finger movements using electrocorticography (ECoG) to control a modular prosthetic limb in real-time [4]. Additionally, a tetraplegic patient was able to achieve upper-limb movements with eight degrees of freedom during various reach-and-touch tasks and wrist rotations using an epidural ECoG-BCI [5]. Another innovative approach involves a hybrid electroencephalography (EEG)/electrooculography-driven hand exoskeleton, which enables quadriplegics to restore intuitive control of hand movements necessary for activities of daily living (ADLs) [6].

Advances in BCI-based neuroprostheses hold the promise of helping individuals with hand paralysis regain dexterity in finger movements. While invasive solutions are nearing this goal [7,8,9,10,11], non-invasive approaches, such as those using EEG, remain less effective [6, 12, 13]. This disparity is primarily due to the superior spatial resolution, spectral bandwidth, and signal-to-noise ratio (SNR) offered by invasive recordings [14, 15]. Nevertheless, EEG systems offer significant advantages: they are non-invasive, even portable, and generally more affordable than other brain-recording systems, while providing acceptable time and spatial resolution. These qualities make EEG-BCI a promising tool for neurorehabilitation. However, functional magnetic resonance imaging has shown that, although there is a small distributed finger-specific somatotopy in the human motor cortex, each digit shares overlapping representations [16, 17]. This overlap makes decoding finger movements inherently challenging. Recent advances in machine learning have enabled high-performance decoding from invasive recordings [8, 9, 11], prompting renewed interest in EEG. Recognizing that ADLs heavily depend on unimanual finger movements, we identified the need to investigate the potential of EEG in decoding fine single- (individual) and multi- (coordinated) finger movements of the same hand.

Movement can lead to either a decrease or an increase in the synchrony of underlying neuronal populations, known respectively as event-related desynchronization (ERD) and event-related synchronization (ERS) [18]. With EEG recordings, finger movements induce alpha and beta ERD prior to movement onset over the contralateral Rolandic region, which become bilaterally symmetrical immediately before movement execution. Beta ERS occurs upon movement termination, while the Rolandic alpha rhythm remains desynchronized. For a comprehensive review, we refer to [18]. Previous research has shown that the strength and spatial distribution of ERD/ERS encode critical information about hand movements, including kinematics, kinetics [19, 20], and movement types [21]. Regarding finger movements, Pfurtscheller et al. found that pre-movement alpha (10–12 Hz) ERD is similar for the index finger, thumb, and hand movements, but differs for later stages [18, 22]. Additionally, the post-movement beta ERS for fingers is significantly smaller compared to the whole hand. Ultra-high-density EEG studies have demonstrated finger-specific ERD/ERS representations, suggesting EEG could provide discriminating information crucial for decoding finger movements [12, 23,24,25].

Unlike ERD/ERS, which reflect power changes, movement-related cortical potentials (MRCPs) are prominent in the low-frequency band (e.g., 0.3–3 Hz) and can be easily visualized when performing or attempting movements [26, 27]. MRCPs are characterized by Bereitschaftspotential (BP) or readiness potential, and reafferent potential [26]. For finger-related movements, MRCPs typically feature an early bilateral negativity (early BP) starting around 3 s before movement onset, followed by a steeper negative slope (late BP) over the contralateral hemisphere about 0.5 s before movement onset [28]. Different hand movements induce characteristic MRCP patterns, allowing for differentiation [27, 29, 30]. However, MRCPs for different finger movements, particularly unimanual ones, are less studied. Quandt et al. pioneered decoding individual unimanual finger movements (thumb, index, middle, and little finger) using EEG and magnetoencephalography (MEG) recordings [31]. They observed that amplitude variations in time series provided the best information for discriminating finger movements, outperforming frequency band oscillations. This suggests that the MRCP profile contains rich information on unimanual finger movements.

Our brain supports a diverse repertoire of finger movements, including both individual and coordinated actions. It is important to determine whether these movements exhibit distinct and decodable EEG correlates, such as ERD/ERS and MRCPs. To date, no EEG study has systematically reported these neural correlates, leaving their potential in decoding fine finger movements largely unexplored. This study aims to investigate the EEG correlates of various unimanual finger movements, ranging from individual to coordinated ones. We focus on non-repetitive finger flexion and extension, simulating real-world grasping scenarios. This straightforward task design allows us to assess the limitations of EEG decoding, as complex (repetitive or rhythmic) finger movements are typically associated with stronger brain activation [32, 33]. While we anticipate some overlap in EEG correlates within the repertoire of finger movements, we expect to discern distinct ones that can serve as discriminative features for decoding. Our findings yield significant implications for the design of dexterous EEG-actuated finger neuroprostheses, potentially enhancing the quality of life of individuals with neuromuscular disorders. By identifying and decoding these EEG correlates, we can advance the development of more effective and precise neuroprosthetic devices.

The latest here:

Effectiveness of mixed reality-based rehabilitation on hands and fingers by individual finger-movement tracking in patients with stroke

 Actually being able to individually move fingers doesn't work when you have spasticity. So this research leaves survivors behind. They obviously were testing on high functioning survivors.

Effectiveness of mixed reality-based rehabilitation on hands and fingers by individual finger-movement tracking in patients with stroke

• Yeajin Ham,
• Dong-Seok Yang,
• Younggeun Choi &
• Joon-Ho Shin 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 140 (2024) Cite this article

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Abstract

Background

Mixed reality (MR) is helpful in hand training for patients with stroke, allowing them to fully submerge in a virtual space while interacting with real objects. The recognition of individual finger movements is required for MR rehabilitation. This study aimed to assess the effectiveness of updated MR-board 2, adding finger training for patients with stroke.

Methods

Twenty-one participants with hemiplegic stroke (10 with left hemiplegia and 11 with right hemiplegia; nine female patients; 56.7 ± 14.2 years of age; and onset of stroke 32.7 ± 34.8 months) participated in this study. MR-board 2 comprised a board plate, a depth camera, plastic-shaped objects, a monitor, a palm-worn camera, and seven gamified training programs. All participants performed 20 self-training sessions involving 30-min training using MR-board 2. The outcome measurements for upper extremity function were the Fugl–Meyer assessment (FMA) upper extremity score, repeated number of finger flexion and extension (Repeat-FE), the thumb opposition test (TOT), Box and Block Test score (BBT), Wolf Motor Function Test score (WMFT), and Stroke Impact Scale (SIS). One-way repeated measures analysis of variance and the post hoc test were applied for the measurements. MR-board 2 recorded the fingers’ active range of motion (AROM) and Dunnett’s test was used for pairwise comparisons.

Results

Except for the FMA-proximal score (p = 0.617) and TOT (p = 0.005), other FMA scores, BBT score, Repeat-FE, WMFT score, and SIS stroke recovery improved significantly (p < 0.001) during MR-board 2 training and were maintained until follow-up. All AROM values of the finger joints changed significantly during training (p < 0.001).

Conclusions

MR-board 2 self-training, which includes natural interactions between humans and computers using a tangible user interface and real-time tracking of the fingers, improved upper limb function across impairment, activity, and participation. MR-board 2 could be used as a self-training tool for patients with stroke, improving their quality of life.

Trial registration number: This study was registered with the Clinical Research Information Service (CRIS: KCT0004167).

Background

Stroke is a prevalent, severe, and incapacitating worldwide health issue, and a key component of stroke care is rehabilitation [1]. Continuous and sufficient rehabilitation is required to elicit functional improvement [2]. Several augmented and virtual reality applications have been implemented to enhance rehabilitation [3]. Mixed reality (MR), which blends virtual reality and physical things, allows participants to fully submerge themselves into a virtual space by interacting with real objects, thereby maintaining their sense of reality. Previous studies have demonstrated the feasibility of MR-based rehabilitation (MRR) specifically for upper limb rehabilitation among participants with stroke [4, 5]. The real physical objects of MRR play the role of tangible user interfaces, enabling more engagement, active participation, and effective learning [6, 7]. MRR could be useful for hand rehabilitation because the physical interfaces provide a haptic sense to the contacting hand, which is a gate for the interaction of the body with objects [8].

Finger individuation can be impaired even by small or lacunar lesions resulting from a stroke [9]. This impaired individuation affects a range of activities, such as typing, grasping, and transporting of objects [10]. Reduced finger strength and impaired finger individuation are two motor deficits affecting hand function following stroke [11]. The potential benefits of the MRR can be achieved through complex hand movement that require individual finger movements. Colomer et al. presented an MRR program that included finger tapping, pincer grasping, and mass grasping [5]. However, recognizing individual finger movements is challenging in previously introduced MR systems because they are only sensed using a depth-perception camera, not collecting kinematic data [5, 8]. Capturing the entire finger movement is particularly difficult for stroke participants because they commonly experience spasticity, dystonia, or deformities, which impede adequate movement perception from the camera [12, 13]. Various types of sensors, including wearable and flexible sensors and inertial measurement unit (IMU) sensors, have been used for fingers [14,15,16]. However, sensing using an IMU sensor is affected by attachment location, and wearable-type sensors are difficult to wear by participants with stroke.

To address these issues, we updated the MRR system (MR-board 2) by adding a palm camera (TapSix) and specific training programs for fingers [17]. We originally developed an MR board for hand rehabilitation and demonstrated the feasibility of the MR board as a self-training tool for the upper extremity in patients with stroke [8]. The MR board provided interventions regarding gross hand movements only and did not include individual finger training (FT). The newly developed MR-board 2 can provide finger-relevant training, allowing for more hierarchical training according to the participants’ capabilities and goals. When participants could not train their fingers at the initial stage, they received gross hand training, such as grasping, releasing, and object manipulation. If they regain finger function, they can move on to individual FT.

Therefore, we hypothesized that MR-board 2 could benefit upper-limb self-rehabilitation, especially for hand rehabilitation, including FT and capturing entire finger movements. This study aimed to apply MR-board 2 to participants with stroke as a tool for self-rehabilitation and explore its effectiveness across every domain (impairment, limitation, and restriction) of the International Classification of Functioning, Disability, and Health (ICF) [18]. We also recorded and analyzed each joint involved in the entire finger movement during FT.

More at link.

Ziploc opening

 Another failure point. I have to use my teeth to get the bags open at least for the older versions, not the sliding zipper. This goes against what my physical therapist ex used to say to me. I wasn't supposed to use my teeth for compensation. Bullshit. She totally knew nothing about stroke rehab. 16 years later and I still have no pinch grip or finger movement in my left hand. I see nothing that will help me recover that.

Finger State Progress Model for Virtual Fine Motor Stroke Rehabilitation

Wow, using motion sensors to capture objective movement problems. When will your stroke hospital  move into the 21st century? 50 years from now?

 Finger State Progress Model for Virtual Fine Motor Stroke Rehabilitation

Mohd Amir Idzham Iberahim1
, Syadiah Nor Wan Shamsuddin1
, Mokhairi Makhtar1
,
Mohd Nordin Abdul Rahman1
, Nordin Simbak2
1Faculty of Informatics and Computing, University of Sultan Zainal Abidin, Malaysia
2Faculty of Medicine, University of Sultan Zainal Abidin, Malaysia

ABSTRACT

Manual observation in measuring and assessing stroke patient progress in fine motor rehabilitation will lead to inconsistencies especially when the patient is evaluated by different therapists or attends different rehabilitation facilities. In addition, it also increases therapist workload if they need to supervise many patients at the same time. Thus, a model was proposed to capture finger data from motion sensor device usingTime-Based Simplified Denavit-Heartenberg (TS-DH)and the Finger State progress (FSP) model. Actual finger movement was compared with patterns of finger state for real-time evaluation of finger movement progress. The model will assist therapists in real-time or post-exercise evaluation of patient progress and analysis can be done during stroke rehabilitation exercise. As a conclusion, the model can be used efficiently in virtual stroke rehabilitation as real-time indicator or as a long term analysis to compare prior progress.  

Altered Corticomuscular Coherence (CMCoh) Pattern in the Upper Limb During Finger Movements After StrokeAltered Corticomuscular Coherence (CMCoh) Pattern in the Upper Limb During Finger Movements After StrokeAltered Corticomuscular Coherence (CMCoh) Pattern in the Upper Limb During Finger Movements After Stroke

I have zero understanding of anything here. 

Altered Corticomuscular Coherence (CMCoh) Pattern in the Upper Limb During Finger Movements After Stroke

Ziqi Guo^†, Qiuyang Qian^†, Kiufung Wong, Hanlin Zhu, Yanhuan Huang, Xiaoling Hu^* and Yongping Zheng

• Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Background: 

Proximal compensation to the distal movements is commonly observed in the affected upper extremity (UE) of patients with chronic stroke. However, the cortical origin of this compensation has not been well-understood. In this study, corticomuscular coherence (CMCoh) and electromyography (EMG) analysis were adopted to investigate the corticomuscular coordinating pattern of proximal UE compensatory activities when conducting distal UE movements in chronic stroke.

Method: 

Fourteen chronic stroke subjects and 10 age-matched unimpaired controls conducted isometric finger extensions and flexions at 20 and 40% of maximal voluntary contractions. Electroencephalogram (EEG) data were recorded from the sensorimotor area and EMG signals were captured from extensor digitorum (ED), flexor digitorum (FD), triceps brachii (TRI), and biceps brachii (BIC) to investigate the CMCoh peak values in the Beta band. EMG parameters, i.e., the EMG activation level and co-contraction index (CI), were analyzed to evaluate the compensatory muscular patterns in the upper limb.

Result: 

The peak CMCoh with statistical significance (P < 0.05) was found shifted from the ipsilesional side to the contralesional side in the proximal UE muscles, while to the central regions in the distal UE muscle in chronic strokes. Significant differences (P < 0.05) were observed in both peak ED and FD CMCohs during finger extensions between the two groups. The unimpaired controls exhibited significant intragroup differences between 20 and 40% levels in extensions for peak ED and FD CMCohs (P < 0.05). The stroke subjects showed significant differences in peak TRI and BIC CMCohs (P < 0.01). No significant inter- or intra-group difference was observed in peak CMCoh during finger flexions. EMG parameters showed higher EMG activation levels in TRI and BIC muscles (P < 0.05), and higher CI values in the muscle pairs involving TRI and BIC during all the extension and flexion tasks in the stroke group than those in the control group (P < 0.05).

Conclusion: 

The post-stroke proximal muscular compensations from the elbow to the finger movements were cortically originated, with the center mainly located in the contralesional hemisphere.

Introduction

Post-stroke motor recovery is usually associated with the cortical reorganization and adaptive learning experiences (1). Cerebral plasticity is the process by which the human body reorganizes neural networks and pathways after a stroke. Existing studies have found that the majority of motor recovery observed via cerebral plasticity reaches a plateau within the first 6 months after the onset (2, 3). Patients with chronic stroke (first onset over 6 months) regain the independence of the activities of daily living but always sustain upper extremity (UE) motor dysfunctions, e.g., muscle weakness, spasticity, and discoordination (4). Specifically, patients' distal UE segments, e.g., fingers and wrist, usually exhibit poorer functional recovery than the proximal elbow and shoulder parts (5). In our previous study (6, 7), we found that the dyscoordination observed following chronic stroke was particularly evident during distal UE joint motion tasks, and that stroke patients frequently relied on compensatory contractions from proximal UE muscles to substitute for a loss or reduction in hand function. However, Jones concluded that proximal compensations can be mistaken for recovery and constrain the potential motor restoration at the distal segments, leading to “learned non-use” or “learned dis-use” (8). Although such post-stroke behavioral deviation can further exacerbate motor impairments, the interaction between the cortical plasticity in chronic stroke and the dynamic muscular coordination in the upper limb has not yet been well-investigated.

Previous neuroimaging studies on motor restoration after stroke using positron emission tomography (PET), functional magnetic resonance (fMRI) imaging, and transcranial magnetic stimulation (TMS) have identified that post-stroke patients exhibit a reduction in brain activities at the lesioned side and a propensity to recruit the contralesional motor cortex when conducting tasks involving the arms (9–11). However, these methods were limited by the low temporal resolutions to reveal the transient relationship between the cortical and muscular dynamics in the investigation of the post-stroke compensatory mechanism to activate proximal muscle contractions in compensation for distal movements in the upper limb.

Electroencephalogram (EEG) and electromyogram (EMG) can capture faster dynamics in the cortex and peripheral muscles, respectively, comparing with the imaging techniques mentioned above. Furthermore, previous studies have found that the coherence between the two parameters can result in the demonstration of time-based functional connections in the neuromuscular pathways when subjects perform specific motion tasks (12, 13). This also makes it possible to identify the location of cortical sources and trace the neuroplasticity after stroke according to the coherence topography (14). The coherence between EEG and EMG was first described by Salenius et al. (15) and Gerloff et al. (16), who referred to it as corticomuscular coherence (CMCoh) to reflect voluntary descending control from the primary motor cortex to the effector muscles. Coherence can be calculated using both EEG and EMG signals, and it is typically observed within the frequency range of 13–30 Hz (Beta band) during the execution of steady-state isometric contraction and phasic movements (17). The maximum value (i.e., the peak CMCoh) denotes the most significant neuromuscular coupling of the coherent activities and location of the central generator over the whole motor cortex (18, 19). Mima et al. (20) first reported the topographical shift of CMCoh from the lesional side to the contralesional side observed among chronic stroke patients, which may be due to the contribution of lateral and/or medial premotor area control made to the muscles, as suggested by previous PET and electrocorticographic studies (21–23). Furthermore, the neuromuscular coupling between cortical commands and consequent muscle activities indicated by CMCoh values is usually not evident immediately after a stroke; rather, it seems to increase throughout the course of the recovery process gradually. Fang et al. (24) and Larsen et al. (25) reported that the CMCoh values in patients with acute and subacute stroke were weaker than those observed in unimpaired controls, while Chen and colleagues (26) found patients with chronic stroke demonstrated higher CMCoh values from the UE flexors than those in a control group. These studies have consistently indicated that data pertaining to the intensity and location of peak coherence could be employed to estimate the muscle representation areas after neural reorganization following stroke. However, most of the CMCoh studies on stroke patients to date investigating the cerebral-derived control on distal UE segments have been limited to EMG recording from distal muscles, e.g., the extensor carpi radialis muscle (19) or its antagonist muscle flexor carpi radialis (27) in wrist extension at the affected side. Rare studies have employed CMCoh to investigate the contractions of proximal muscles to compensate for distal motions, which could be traced back to a cortical-originated alteration in muscular discoordination at the peripheral.

The purpose of this study was to investigate the corticomuscular coordination pattern in the upper limb muscles during distal finger movements at the affected side of patients with chronic stroke, via a combination of EEG and EMG measurements.