{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/17688"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/17688","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Study on FPGA-based computation units for Ocular Artifact Removal algorithm for multi-channel Electroencephalography (EEG)","abstract":"Introduction: The goal of this research to design and validate detailed computational units required for the implementation of an adaptive noise canceling (ANC) algorithm for ocular artifact removal on Field Programmable Gate Arrays (FPGA) for multi-channel electroencephalography (EEG) signals. The primary objective is to analyze the mathematical representation of the ocular artifact removal algorithm and convert it to hardware implementable computational modules. Input is taken from an existing scalp EEG recording dataset from EEG signal acquisition research at the University of Houston. Methods: Considering the non-stationary nature of ocular artifacts and their adverse effect on EEG signal integrity, the dynamic ANC technique was selected. This technique is based on the H-infinity algorithm, developed by Kilicarslan, Grossman and Contreras-Vidal (2016), renowned for its robust ocular artifact removal capabilities. It also eliminates signal amplitude bias and local/global drifts. The H-infinity algorithm was successfully implemented in hardware using Verilog, incorporating custom divide, 3×3 matrix inversion, and the core H-infinity module. Operating in a 64-bit fixed-point format, the design produced expected outcomes and demonstrated a high correlation with MATLAB-simulated reference results. Discussion: The H-infinity algorithm encompasses multiple computation stages, generating sizable sub-products that can lead to data overflow due to fixed-point number range constraints. Overcoming this limitation involved experimentation to strike the optimal balance between the integer and fractional parts. Through strategic design techniques, implementing this intricate ANC algorithm on FPGA became feasible, leveraging FPGA's parallel processing capabilities and accelerating the H-infinity algorithm on hardware. Significance: This research serves as a foundation for FPGA-based implementation of real-time ANC algorithms for mobile brain-body imaging and brain computer interface applications. This work not only contributes to the technical domain of EEG signal processing and adaptive noise cancelling, but also has the potential to enhance the quality of life for individuals relying on BCI technologies for communication and control, marking a significant step forward in the intersection of neuroscience and technology.","abstract_html":"Introduction: The goal of this research to design and validate detailed computational units required for the implementation of an adaptive noise canceling (ANC) algorithm for ocular artifact removal on Field Programmable Gate Arrays (FPGA) for multi-channel electroencephalography (EEG) signals. The primary objective is to analyze the mathematical representation of the ocular artifact removal algorithm and convert it to hardware implementable computational modules. Input is taken from an existing scalp EEG recording dataset from EEG signal acquisition research at the University of Houston. Methods: Considering the non-stationary nature of ocular artifacts and their adverse effect on EEG signal integrity, the dynamic ANC technique was selected. This technique is based on the H-infinity algorithm, developed by Kilicarslan, Grossman and Contreras-Vidal (2016), renowned for its robust ocular artifact removal capabilities. It also eliminates signal amplitude bias and local/global drifts. The H-infinity algorithm was successfully implemented in hardware using Verilog, incorporating custom divide, 3×3 matrix inversion, and the core H-infinity module. Operating in a 64-bit fixed-point format, the design produced expected outcomes and demonstrated a high correlation with MATLAB-simulated reference results. Discussion: The H-infinity algorithm encompasses multiple computation stages, generating sizable sub-products that can lead to data overflow due to fixed-point number range constraints. Overcoming this limitation involved experimentation to strike the optimal balance between the integer and fractional parts. Through strategic design techniques, implementing this intricate ANC algorithm on FPGA became feasible, leveraging FPGA's parallel processing capabilities and accelerating the H-infinity algorithm on hardware. Significance: This research serves as a foundation for FPGA-based implementation of real-time ANC algorithms for mobile brain-body imaging and brain computer interface applications. This work not only contributes to the technical domain of EEG signal processing and adaptive noise cancelling, but also has the potential to enhance the quality of life for individuals relying on BCI technologies for communication and control, marking a significant step forward in the intersection of neuroscience and technology.","abstract_has_math":false,"creators":["Reddy, Vishal Reddy"],"institution":"University of Houston","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Contreras Vidal, Jose L"],"committee_chairs":[],"committee_members":["Chen, Yuhua","Fu, Xin"],"year":2024,"date_issued":"2024-05-08","date_published":"2024-05-08","updated_at":"2026-07-24T02:32:27Z","subjects":["BCI, EEG, H-infinity filter algorithm, FPGA, Ocular Artifact Removal algorithm"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/17688","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Contreras Vidal, Jose L"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Chen, Yuhua","Fu, Xin"]},{"key":"dc:creator","label":"Author","values":["Reddy, Vishal Reddy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-07-26T04:11:06Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-05-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["BCI, EEG, H-infinity filter algorithm, FPGA, Ocular Artifact Removal algorithm"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/17688"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Introduction: The goal of this research to design and validate detailed computational units required for the implementation of an adaptive noise canceling (ANC) algorithm for ocular artifact removal on Field Programmable Gate Arrays (FPGA) for multi-channel electroencephalography (EEG) signals. The primary objective is to analyze the mathematical representation of the ocular artifact removal algorithm and convert it to hardware implementable computational modules. Input is taken from an existing scalp EEG recording dataset from EEG signal acquisition research at the University of Houston. Methods: Considering the non-stationary nature of ocular artifacts and their adverse effect on EEG signal integrity, the dynamic ANC technique was selected. This technique is based on the H-infinity algorithm, developed by Kilicarslan, Grossman and Contreras-Vidal (2016), renowned for its robust ocular artifact removal capabilities. It also eliminates signal amplitude bias and local/global drifts. The H-infinity algorithm was successfully implemented in hardware using Verilog, incorporating custom divide, 3×3 matrix inversion, and the core H-infinity module. Operating in a 64-bit fixed-point format, the design produced expected outcomes and demonstrated a high correlation with MATLAB-simulated reference results. Discussion: The H-infinity algorithm encompasses multiple computation stages, generating sizable sub-products that can lead to data overflow due to fixed-point number range constraints. Overcoming this limitation involved experimentation to strike the optimal balance between the integer and fractional parts. Through strategic design techniques, implementing this intricate ANC algorithm on FPGA became feasible, leveraging FPGA's parallel processing capabilities and accelerating the H-infinity algorithm on hardware. Significance: This research serves as a foundation for FPGA-based implementation of real-time ANC algorithms for mobile brain-body imaging and brain computer interface applications. This work not only contributes to the technical domain of EEG signal processing and adaptive noise cancelling, but also has the potential to enhance the quality of life for individuals relying on BCI technologies for communication and control, marking a significant step forward in the intersection of neuroscience and technology."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Study on FPGA-based computation units for Ocular Artifact Removal algorithm for multi-channel Electroencephalography (EEG)"]}]}],"canonical_facts":{"dc:contributor.advisor":["Contreras Vidal, Jose L"],"dc:contributor.committeemember":["Chen, Yuhua","Fu, Xin"],"dc:creator":["Reddy, Vishal Reddy"],"dc:date.accessioned":["2024-07-26T04:11:06Z"],"dc:date.issued":["2024-05-08"],"dc:description.abstract":["Introduction: The goal of this research to design and validate detailed computational units required for the implementation of an adaptive noise canceling (ANC) algorithm for ocular artifact removal on Field Programmable Gate Arrays (FPGA) for multi-channel electroencephalography (EEG) signals. The primary objective is to analyze the mathematical representation of the ocular artifact removal algorithm and convert it to hardware implementable computational modules. Input is taken from an existing scalp EEG recording dataset from EEG signal acquisition research at the University of Houston. Methods: Considering the non-stationary nature of ocular artifacts and their adverse effect on EEG signal integrity, the dynamic ANC technique was selected. This technique is based on the H-infinity algorithm, developed by Kilicarslan, Grossman and Contreras-Vidal (2016), renowned for its robust ocular artifact removal capabilities. It also eliminates signal amplitude bias and local/global drifts. The H-infinity algorithm was successfully implemented in hardware using Verilog, incorporating custom divide, 3×3 matrix inversion, and the core H-infinity module. Operating in a 64-bit fixed-point format, the design produced expected outcomes and demonstrated a high correlation with MATLAB-simulated reference results. Discussion: The H-infinity algorithm encompasses multiple computation stages, generating sizable sub-products that can lead to data overflow due to fixed-point number range constraints. Overcoming this limitation involved experimentation to strike the optimal balance between the integer and fractional parts. Through strategic design techniques, implementing this intricate ANC algorithm on FPGA became feasible, leveraging FPGA's parallel processing capabilities and accelerating the H-infinity algorithm on hardware. Significance: This research serves as a foundation for FPGA-based implementation of real-time ANC algorithms for mobile brain-body imaging and brain computer interface applications. This work not only contributes to the technical domain of EEG signal processing and adaptive noise cancelling, but also has the potential to enhance the quality of life for individuals relying on BCI technologies for communication and control, marking a significant step forward in the intersection of neuroscience and technology."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/17688"],"dc:subject":["BCI, EEG, H-infinity filter algorithm, FPGA, Ocular Artifact Removal algorithm"],"dc:title":["Study on FPGA-based computation units for Ocular Artifact Removal algorithm for multi-channel Electroencephalography (EEG)"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:27Z"}