Adaptive Artifact Removal From Intracortical Channels for Accurate Decoding of a Force Signal in Freely Moving Rats

Khorasani, Abed; Shalchyan, Vahid; Daliri, Mohammad Reza

doi:10.3389/fnins.2019.00350

Cited by 16 publications

(12 citation statements)

References 23 publications

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“…An exception to this issue is the Hampel filter, as it operates in the frequency domain ( Allen et al, 2010 ). Signal decomposition methods utilize the similar structure of artifacts across a large number of electrodes in order to remove the signal as a common feature ( Khorasani et al, 2019 ; Mena et al, 2017 ; O’Shea and Shenoy, 2018 ). These methods have shown significant success but require a large number of recording channels to be effective ( Lau et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Rijn

Provenza

Calvert

et al. 2021

Cell Reports Methods

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SUMMARY Advances in therapeutic neuromodulation devices have enabled concurrent stimulation and electrophysiology in the central nervous system. However, stimulation artifacts often obscure the sensed underlying neural activity. Here, we develop a method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker are used for validation. Performance is found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: (1) it is superior in signal recovery; (2) it is easily adaptable to several neurostimulation paradigms; and (3) it has low complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.

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Section: Introductionmentioning

confidence: 99%

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Rijn

Provenza

Calvert

et al. 2021

Cell Reports Methods

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“…Kalman filter was applied to find weight vectors for each channel. Then in order to estimate weights, zi(t) signals are expressed in a discrete-time Markovian state-space model [35].…”

Section: Weighted Common Average Reference (Wcar) Filter Methodsmentioning

confidence: 99%

“…The Kalman gain can be repeated according to the equation above. For more detailed information about WCAR, the study conducted by Khorasani et al [35] can be examined.…”

Section: ) Status Update Depending On the Previous Situationmentioning

confidence: 99%

“…The accuracy rate belonging to the most popular and simple model was examined to determine how successful the proposed model was. This ratio was defined as the ratio of the number of correctly classified (TP + TN) samples to the total number of samples (TP + TN + FP + FN) [35]. Sometimes, on the contrary, the performance rate of the model is found by determining the error rate, which is expressed as the ratio of the number of incorrectly classified samples to the total number of samples [36].…”

Section: Performance Criteriamentioning

confidence: 99%

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Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease

Özel¹,

Tekin²,

Kaya³

2021

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Parkinson's disease (PD) is a neurological disease that progresses further over time. Individuals suffering from this condition have a deficiency of dopamine, a neurotransmitter found in the brain's nerve cells that is critical for coordinating body movement. In this study, a new approach is proposed for the diagnosis of PD. Common Average Reference (CAR), Median Common Average Reference (MCAR), and Weighted Common Average Reference (WCAR) methods were primarily utilized to eliminate noise from the multichannel recorded walking signals in the resulting PhysioNet dataset. Statistical features were obtained from the clean walking signals following the Local Binary Pattern (LBP) transformation application. Logistic Regression (LR), Random Forest (RF), and K-nearest neighbor (Knn) methods were utilized in the classification stage. A high success rate with a value of 92.96% was observed with Knn. It was also determined that signals on which foot and the signals obtained from which point of the sole of the foot were effective in PD diagnosis in the study. In light of the findings, it was observed that noise reduction methods increased the success rate of PD diagnosis.

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“…For instance, Khorasani et al . proposed a novel adaptive artifact removal technique for enhancing signal quality to achieve higher performance in BCI [ 13 ]. Foodeh et al .…”

Section: Introductionmentioning

confidence: 99%

A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1)

Kashefi

Daliri

2021

BMC Bioinformatics

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Background Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal. Results The performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ($$R^{2}$$ R 2 ) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was able to accurately decode the force values without explicitly using time lags in the input features. Additionally, the proposed method was able to predict zero-force values very accurately due to benefiting from an output nonlinearity. Conclusion The proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.

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Adaptive Artifact Removal From Intracortical Channels for Accurate Decoding of a Force Signal in Freely Moving Rats

Cited by 16 publications

References 23 publications

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease

A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1)

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