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
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 228 (2024)
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.
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